Triple
T5277230
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Minion Cafe |
E119398
|
entity |
| Predicate | hasEntertainmentElement |
P51190
|
FINISHED |
| Object | character references in food names |
—
|
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: character references in food names | Statement: [Minion Cafe, hasEntertainmentElement, character references in food names]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEntertainmentElement Context triple: [Minion Cafe, hasEntertainmentElement, character references in food names]
-
A.
hasEntertainmentOptions
chosen
Indicates that an entity provides or includes one or more forms of entertainment or leisure activities as options.
-
B.
hasComedyElements
Indicates that something contains humorous or comedic aspects as part of its overall content or style.
-
C.
hasQueueEntertainment
Indicates that an entity provides or features entertainment specifically for people waiting in a queue.
-
D.
hasEntertainmentVenue
Indicates that an entity possesses, contains, or is associated with an entertainment venue as part of its facilities or offerings.
-
E.
hasRealityTVElement
Indicates that something includes characteristics, themes, or stylistic features commonly associated with reality television.
- 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_69bd446d05a8819092ad333a3f9c8d5c |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd8c9c72b08190947b6b955ac1bb5a |
completed | March 20, 2026, 6:06 p.m. |
| PD | Predicate disambiguation | batch_69bd844a56b48190ad743c42246e02dd |
completed | March 20, 2026, 5:30 p.m. |
Created at: March 20, 2026, 1:51 p.m.