Triple
T7742111
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dopey |
E175534
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object | Doc |
E173049
|
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: Doc | Statement: [Dopey, associatedWith, Doc]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Doc Context triple: [Dopey, associatedWith, Doc]
-
A.
Doc
Doc is the widely used nickname of Glenn "Doc" Rivers, a former NBA player and championship-winning head coach.
-
B.
Doc
chosen
Doc is one of the seven dwarfs in Disney's "Snow White and the Seven Dwarfs," characterized as their kindly, bearded leader who often fumbles his words.
-
C.
Doc
Doc is a gentle, eccentric marine biologist in John Steinbeck’s novel "Cannery Row," known for his intelligence, compassion, and central role in the community’s life.
-
D.
Doc
Doc is the longtime play-by-play announcer Mike "Doc" Emrick, renowned for his iconic voice and decades of work calling National Hockey League games.
-
E.
Doc
Doc is the central character in the 1938 horse-racing drama film "Stablemates," around whom the story’s key events and relationships revolve.
- 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_69c6995f9c60819092e386192bd63c6f |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c70387807081909546bc7c209955ef |
completed | March 27, 2026, 10:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8c7c63c688190ac257a738759d59f |
completed | March 29, 2026, 6:33 a.m. |
Created at: March 27, 2026, 4:07 p.m.