Triple
T656317
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bruce Dern |
E11656
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Monster |
E50475
|
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: Monster | Statement: [Bruce Dern, notableWork, Monster]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monster Context triple: [Bruce Dern, notableWork, Monster]
-
A.
Monster
chosen
Monster is a 2003 biographical crime drama film in which Charlize Theron delivers an Oscar-winning performance as serial killer Aileen Wuornos.
-
B.
Monster
Monster is a town in the Dutch province of South Holland, known for its coastal location near the North Sea and its greenhouse horticulture.
-
C.
Planet Terror
Planet Terror is a 2007 grindhouse-style zombie action-horror film written and directed by Robert Rodriguez, known for its over-the-top gore, dark humor, and retro exploitation aesthetic.
-
D.
Kaiju
Kaiju are colossal, monstrous creatures from Japanese science fiction and popular culture, often depicted as city-destroying beasts that battle humanity or other giant monsters.
-
E.
Monsters University
Monsters University is a 2013 Pixar animated prequel to Monsters, Inc. that follows Mike and Sulley’s college years as they train to become professional scarers.
- 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_69a4932862a0819098be659c814e4981 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49f4e87408190b5276d2b913d0426 |
completed | March 1, 2026, 8:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a5914abe2c8190a27f520f445554d8 |
completed | March 2, 2026, 1:31 p.m. |
Created at: March 1, 2026, 7:36 p.m.