Triple
T31983836
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chad McQueen |
E816661
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Death Ring
Death Ring is a 1992 action film starring Chad McQueen as a former soldier forced into a deadly manhunt game on a private island.
|
E1987864
|
NE FINISHED |
How this triple was built (2 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: Death Ring | Statement: [Chad McQueen, notableWork, Death Ring]
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: Death Ring Triple: [Chad McQueen, notableWork, Death Ring]
Generated description
Death Ring is a 1992 action film starring Chad McQueen as a former soldier forced into a deadly manhunt game on a private island.
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_69f348f6a3008190bfb59ca695fd68e2 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6b3ad0d0481909f2cf7d931a3a418 |
completed | May 3, 2026, 2:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a2eb152c2bc8190991f3fa524068e12 |
completed | June 14, 2026, 1:49 p.m. |
| NEDg | Description generation | batch_6a2eb4d3e8788190836f22cf19bfefdc |
completed | June 14, 2026, 2:04 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a2ece6e2954819081d3ae8fe1a0fb95 |
completed | June 14, 2026, 3:53 p.m. |
Created at: May 1, 2026, 12:12 a.m.