Triple
T14523291
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thomas Jane |
E340705
|
entity |
| Predicate | playedIn |
P2170
|
FINISHED |
| Object |
Stander
Stander is a 2003 crime drama film based on the true story of South African police officer-turned-bank robber André Stander.
|
E1104531
|
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: Stander | Statement: [Thomas Jane, playedIn, Stander]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stander Context triple: [Thomas Jane, playedIn, Stander]
-
A.
Tamu
Tamu is a town in northwestern Myanmar’s Sagaing Region, situated near the India–Myanmar border and serving as an important cross-border trade and transit point.
-
B.
Creighton
Creighton is a masculine given name most notably associated with U.S. Army General Creighton Abrams.
-
C.
Doane
Doane is a surname most notably associated with William Croswell Doane, the first Episcopal Bishop of Albany and a prominent 19th-century American church leader.
-
D.
Baylor
Baylor is a surname most notably associated with American baseball player and manager Don Baylor.
-
E.
Tarkio College
Tarkio College was a small liberal arts college in Tarkio, Missouri, known for educating future DuPont chemist and nylon inventor Wallace H. Carothers.
- 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: Stander Triple: [Thomas Jane, playedIn, Stander]
Generated description
Stander is a 2003 crime drama film based on the true story of South African police officer-turned-bank robber André Stander.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Stander Target entity description: Stander is a 2003 crime drama film based on the true story of South African police officer-turned-bank robber André Stander.
-
A.
Tamu
Tamu is a town in northwestern Myanmar’s Sagaing Region, situated near the India–Myanmar border and serving as an important cross-border trade and transit point.
-
B.
Creighton
Creighton is a masculine given name most notably associated with U.S. Army General Creighton Abrams.
-
C.
Doane
Doane is a surname most notably associated with William Croswell Doane, the first Episcopal Bishop of Albany and a prominent 19th-century American church leader.
-
D.
Baylor
Baylor is a surname most notably associated with American baseball player and manager Don Baylor.
-
E.
Tarkio College
Tarkio College was a small liberal arts college in Tarkio, Missouri, known for educating future DuPont chemist and nylon inventor Wallace H. Carothers.
- 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_69d822dac79c8190a84a073f3cbaced5 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dea04f16f88190ba357b0f8021b46b |
completed | April 14, 2026, 8:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd7a50324481909713bbf68295e839 |
completed | May 8, 2026, 5:53 a.m. |
| NEDg | Description generation | batch_69fd7c52e0bc8190b8c4b270653e65df |
completed | May 8, 2026, 6:01 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd7cf3088c8190a3bf53c9599f0304 |
completed | May 8, 2026, 6:04 a.m. |
Created at: April 10, 2026, 1:22 a.m.