Triple
T6559511
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daniel Auster |
E152541
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Daniel Auster |
E152541
|
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: Daniel Auster | Statement: [Daniel Auster, name, Daniel Auster]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daniel Auster Context triple: [Daniel Auster, name, Daniel Auster]
-
A.
Daniel Auster
chosen
Daniel Auster was a prominent Zionist politician and lawyer who served as mayor of Jerusalem during the British Mandate period.
-
B.
Daniel Ullman
Daniel Ullman was an American screenwriter known for his work on mid-20th-century genre films, particularly Westerns and thrillers.
-
C.
Len Blum
Len Blum is a Canadian screenwriter known for his work on numerous comedy films, including the 2006 reboot of The Pink Panther.
-
D.
Alan Siegel
Alan Siegel is a film producer best known for his long-running collaboration with actor Gerard Butler on action and thriller movies.
-
E.
David Wechter
David Wechter is an American screenwriter and filmmaker best known for co-writing genre films such as the sci-fi horror movie "The Faculty."
- 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_69c688058d6881908c19b309cc55dbfa |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6ae22442081909bd6e2ba0091c56b |
completed | March 27, 2026, 4:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6cb8bae88819089cff70fa1101a39 |
completed | March 27, 2026, 6:25 p.m. |
Created at: March 27, 2026, 1:52 p.m.