Triple
T3200587
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chef Mickey's |
E67038
|
entity |
| Predicate | allowsCharacterAutographs |
P30896
|
FINISHED |
| Object | true |
—
|
LITERAL 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: true | Statement: [Chef Mickey's, allowsCharacterAutographs, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: allowsCharacterAutographs Context triple: [Chef Mickey's, allowsCharacterAutographs, true]
-
A.
signedArtist
Indicates that an artist has entered into a formal contract or agreement with a label, company, or representative.
-
B.
hasSignFor
Indicates that one entity displays, bears, or provides a sign, symbol, or notice that represents, directs attention to, or gives information about another entity.
-
C.
hasFoilCharacters
Indicates that one character is contrasted with another to highlight their differing traits, qualities, or roles.
-
D.
featuresCharacterMeetAndGreets
chosen
Indicates that the subject offers opportunities for visitors to meet and interact with characters in organized meet-and-greet sessions.
-
E.
hasInscriptions
Indicates that an object, surface, or artifact bears written, carved, or engraved inscriptions on it.
- F. None of above.
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_69ad8589bd988190afa7ed2bdffb7b33 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada9aedef08190824bdf508f85f06f |
completed | March 8, 2026, 4:54 p.m. |
| PD | Predicate disambiguation | batch_69ad9e05e4f48190adbe4366cdba2349 |
completed | March 8, 2026, 4:04 p.m. |
Created at: March 8, 2026, 3:07 p.m.