Triple
T15063539
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hard Target |
E379697
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object | Chuck Pfarrer |
E679069
|
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: Chuck Pfarrer | Statement: [Hard Target, screenwriter, Chuck Pfarrer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chuck Pfarrer Context triple: [Hard Target, screenwriter, Chuck Pfarrer]
-
A.
Chuck Pfarrer
chosen
Chuck Pfarrer is an American screenwriter, author, and former Navy SEAL known for his work on action and thriller films as well as military-themed nonfiction books.
-
B.
Ken Schretzmann
Ken Schretzmann is a film editor known for his work on major animated features, including Guillermo del Toro's stop-motion adaptation of Pinocchio.
-
C.
Josh Schaeffer
Josh Schaeffer is a film editor known for his work on major studio features, including the monster crossover blockbuster "Godzilla vs. Kong."
-
D.
Keith Poulson
Keith Poulson is an American actor known for his work in independent films and for roles in offbeat, character-driven movies.
-
E.
Matt Oberg
Matt Oberg is an American actor and comedian known for his work in television comedies and voice acting roles.
- 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_69d85cd7683881908d405c1b5d7b4f7f |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69dedee803ac81908bb7d66e49c2eb72 |
completed | April 15, 2026, 12:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69feef61cf80819096a3cb611f5af9fc |
completed | May 9, 2026, 8:25 a.m. |
Created at: April 10, 2026, 3:02 a.m.