Triple
T21005916
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thor franchise |
E517412
|
entity |
| Predicate | notableDirector |
P4744
|
FINISHED |
| Object | Alan Taylor |
—
|
NE NERFINISHED |
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: Alan Taylor | Statement: [Thor franchise, notableDirector, Alan Taylor]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alan Taylor Context triple: [Thor franchise, notableDirector, Alan Taylor]
-
A.
Alan Taylor
chosen
Alan Taylor is an American film and television director known for his work on major projects such as "Game of Thrones," "Thor: The Dark World," and "Terminator Genisys."
-
B.
Alan Taylor
Alan Taylor is an American historian renowned for his influential works on early American history, for which he has twice won the Pulitzer Prize for History.
-
C.
J. O. Taylor
J. O. Taylor was a cinematographer active during early Hollywood who worked on the landmark 1933 monster film "King Kong."
-
D.
Daniel Walker Howe
Daniel Walker Howe is an American historian best known for his Pulitzer Prize–winning work on early 19th-century United States history.
-
E.
Douglas Brinkley
Douglas Brinkley is an American historian, author, and professor known for his works on U.S. political and cultural history and for serving as a frequent commentator on historical issues in the media.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b50192308190a284fcc89dd23a49 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fc3c05a481908d25de2a63a4cdbe |
completed | April 21, 2026, 4:25 a.m. |
Created at: April 16, 2026, 1:52 p.m.