Triple
T15355396
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Comnidyne |
E367159
|
entity |
| Predicate | associatedWithCharacter |
P1481
|
FINISHED |
| Object | Dave Harken |
E367158
|
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: Dave Harken | Statement: [Comnidyne, associatedWithCharacter, Dave Harken]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dave Harken Context triple: [Comnidyne, associatedWithCharacter, Dave Harken]
-
A.
Dave Harken
chosen
Dave Harken is the tyrannical, manipulative boss and main antagonist portrayed by Kevin Spacey in the dark comedy film "Horrible Bosses."
-
B.
Stephen Kunken
Stephen Kunken is an American actor known for his work in film, television, and theater, including roles in projects like the Woody Allen film "Café Society" and the TV series "Billions."
-
C.
Dan Harrow
Dan Harrow is the earnest, idealistic young farmer who serves as the central romantic lead in the stage musical and film "The Farmer Takes a Wife."
-
D.
Mike Krieger
Mike Krieger is a Brazilian-American entrepreneur and software engineer best known as the co-founder and former CTO of the photo-sharing social media platform Instagram.
-
E.
Mike Gunton
Mike Gunton is a British television producer best known for his work on landmark BBC natural history documentaries.
- 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_69d85a1483788190ad93c2748e8af34b |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e2c00648190ae2325e1ee58dcfd |
completed | April 16, 2026, 1:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff4c2efb148190a2e6c0811f2afb5f |
completed | May 9, 2026, 3:01 p.m. |
Created at: April 10, 2026, 3:18 a.m.