Triple
T14653561
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stronger |
E344051
|
entity |
| Predicate | director |
P255
|
FINISHED |
| Object | David Gordon Green |
E326521
|
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: David Gordon Green | Statement: [Stronger, director, David Gordon Green]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: David Gordon Green Context triple: [Stronger, director, David Gordon Green]
-
A.
David Gordon Green
chosen
David Gordon Green is an American filmmaker known for his eclectic career spanning lyrical independent dramas like "George Washington" and mainstream comedies and horror franchises such as "Pineapple Express" and the recent "Halloween" trilogy.
-
B.
Jason Reitman
Jason Reitman is a Canadian-American filmmaker known for his sharp, character-driven comedies and dramas such as "Juno," "Up in the Air," and "Thank You for Smoking."
-
C.
Alex Gansa
Alex Gansa is an American television writer and producer best known for co-creating and showrunning the acclaimed political thriller series "Homeland."
-
D.
Paul Thomas Anderson
Paul Thomas Anderson is an acclaimed American filmmaker known for his character-driven, stylistically distinctive films such as "Boogie Nights," "Magnolia," and "There Will Be Blood."
-
E.
Martin Arjovsky
Martin Arjovsky is a machine learning researcher best known for introducing the Wasserstein GAN, a generative adversarial network variant that improves training stability and sample quality.
- 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_69d822e1a2cc81908e5bb93cf61ce3cc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb518f7dc8190877997ea4cd3eed2 |
completed | April 14, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdf07acd748190b2821dee21ecd740 |
completed | May 8, 2026, 2:17 p.m. |
Created at: April 10, 2026, 1:27 a.m.