Triple
T6958077
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jeremy Thomas |
E161297
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Jeremy Thomas |
E161297
|
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: Jeremy Thomas | Statement: [Jeremy Thomas, name, Jeremy Thomas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeremy Thomas Context triple: [Jeremy Thomas, name, Jeremy Thomas]
-
A.
Jeremy Thomas
chosen
Jeremy Thomas is a renowned British film producer known for his work on acclaimed independent and art-house films, including multiple collaborations with prominent directors.
-
B.
Dean Thomas
Dean Thomas is a fictional Gryffindor student and member of Dumbledore's Army in J.K. Rowling's Harry Potter series.
-
C.
John Thomas
John Thomas is an American cinematographer best known for his work on the Sex and the City television series and its film adaptations.
-
D.
John Thomas
John Thomas is an author known for writing about the Predator franchise.
-
E.
Tony Thomas
Tony Thomas is an American television and film producer best known for co-founding Witt/Thomas Productions and producing popular sitcoms such as "The Golden Girls."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c68852a9a0819097797e31d492e273 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6dad240ac8190808014a5b4920b41 |
completed | March 27, 2026, 7:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7618df8648190bec6c0aaca312efd |
completed | March 28, 2026, 5:05 a.m. |
Created at: March 27, 2026, 2:29 p.m.