Triple
T4356084
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Die Hard |
E98150
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | John F. Link |
E296716
|
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: John F. Link | Statement: [Die Hard, editedBy, John F. Link]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: John F. Link Context triple: [Die Hard, editedBy, John F. Link]
-
A.
John F. Link
chosen
John F. Link is a film editor known for his work on various Hollywood movies, including action and comedy films.
-
B.
Alan M. Garber
Alan M. Garber is an American physician-economist and academic leader known for his work in health policy and for serving in top administrative roles at Harvard University.
-
C.
Neil A. Machlis
Neil A. Machlis is a film producer best known for his work on major Hollywood comedies, including the classic road-trip film "Planes, Trains and Automobiles."
-
D.
Daniel P. Hanley
Daniel P. Hanley is an American film editor best known for his long-time collaboration with director Ron Howard on numerous major Hollywood films.
-
E.
George A. Bermann
George A. Bermann is a prominent American legal scholar and expert in international and comparative law, particularly known for his work in international arbitration.
- 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_69b3454965f881908c41190bb22f0e4b |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b351c5773481908446d84897e7a533 |
completed | March 12, 2026, 11:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bfce9ced708190b3ed8f9708c6f519 |
completed | March 22, 2026, 11:12 a.m. |
Created at: March 12, 2026, 11:16 p.m.