Triple
T9762189
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sunnyside Daycare |
E236695
|
entity |
| Predicate | associatedWithCharacter |
P1481
|
FINISHED |
| Object | Woody |
E62654
|
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: Woody | Statement: [Sunnyside Daycare, associatedWithCharacter, Woody]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Woody Context triple: [Sunnyside Daycare, associatedWithCharacter, Woody]
-
A.
Woody
Woody is the commonly used nickname of American businessman and New York Jets owner Woody Johnson.
-
B.
Woody
Woody is the nickname of Woody Guthrie, the influential American folk singer-songwriter known for his protest music and the anthem "This Land Is Your Land."
-
C.
Woody in Toy Story
chosen
Woody in Toy Story is the loyal, cowboy doll leader of Andy’s toys and the central protagonist of Pixar’s Toy Story film series.
-
D.
Woody Omens
Woody Omens is an American cinematographer best known for his work on films such as the 1989 crime-comedy "Harlem Nights."
-
E.
Woody's steed
Bullseye is the loyal toy horse from the Toy Story franchise who serves as Woody’s faithful companion and mount.
- 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_69ca84d64f6c8190a4ed4e9f5936eda5 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda04c70108190a8ed09eb6f2a124e |
completed | April 1, 2026, 10:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1e4064c8c8190b18f419135f049a9 |
completed | April 5, 2026, 4:24 a.m. |
Created at: March 30, 2026, 8:25 p.m.