Triple
T7201008
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cassidy Gifford |
E168740
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Time Trap
Time Trap is a 2017 science fiction adventure film about a group of students who discover a cave where time passes at drastically different speeds.
|
E648753
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Time Trap | Statement: [Cassidy Gifford, notableWork, Time Trap]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Time Trap Context triple: [Cassidy Gifford, notableWork, Time Trap]
-
A.
The Trap
"The Trap" is a horror novel by Tabitha King that delves into psychological terror and the darker sides of human relationships in a small-town setting.
-
B.
The Trap
The Trap is a 1966 British adventure drama film set in the Canadian wilderness, starring Rita Tushingham and Oliver Reed.
-
C.
The Steel Trap
The Steel Trap is a 1952 American crime thriller film starring Joseph Cotten as a bank employee who devises a plan to steal money and flee the country.
-
D.
Moontrap
Moontrap is a 1989 science fiction horror film starring Walter Koenig and Bruce Campbell, featuring astronauts who encounter ancient alien technology on the Moon.
-
E.
Traps
"Traps" is a novel by MacKenzie Scott (formerly MacKenzie Bezos), known for its interwoven narratives about four women whose lives collide over a tense four-day period.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Time Trap Triple: [Cassidy Gifford, notableWork, Time Trap]
Generated description
Time Trap is a 2017 science fiction adventure film about a group of students who discover a cave where time passes at drastically different speeds.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Time Trap Target entity description: Time Trap is a 2017 science fiction adventure film about a group of students who discover a cave where time passes at drastically different speeds.
-
A.
The Trap
"The Trap" is a horror novel by Tabitha King that delves into psychological terror and the darker sides of human relationships in a small-town setting.
-
B.
The Trap
The Trap is a 1966 British adventure drama film set in the Canadian wilderness, starring Rita Tushingham and Oliver Reed.
-
C.
The Steel Trap
The Steel Trap is a 1952 American crime thriller film starring Joseph Cotten as a bank employee who devises a plan to steal money and flee the country.
-
D.
Moontrap
Moontrap is a 1989 science fiction horror film starring Walter Koenig and Bruce Campbell, featuring astronauts who encounter ancient alien technology on the Moon.
-
E.
Traps
"Traps" is a novel by MacKenzie Scott (formerly MacKenzie Bezos), known for its interwoven narratives about four women whose lives collide over a tense four-day period.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69c68a5376748190bb500f03df86e93e |
completed | March 27, 2026, 1:46 p.m. |
| NER | Named-entity recognition | batch_69c6e94971508190bb38184c9af2fe51 |
completed | March 27, 2026, 8:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7bfb0f4688190a677b818a0b3619c |
completed | March 28, 2026, 11:46 a.m. |
| NEDg | Description generation | batch_69c7c09957d881909847e8bf3cf25b92 |
completed | March 28, 2026, 11:50 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c7c123c4b48190a76fb869f7abc553 |
completed | March 28, 2026, 11:53 a.m. |
Created at: March 27, 2026, 2:52 p.m.