Triple
T4383445
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cape May Cafe |
E99183
|
entity |
| Predicate | offersCharacterMeetAndGreets |
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: [Cape May Cafe, offersCharacterMeetAndGreets, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: offersCharacterMeetAndGreets Context triple: [Cape May Cafe, offersCharacterMeetAndGreets, true]
-
A.
featuresCharacterMeetAndGreets
chosen
Indicates that the subject offers opportunities for visitors to meet and interact with characters in organized meet-and-greet sessions.
-
B.
hasIndoorMeetAndGreet
Indicates that an entity offers an indoor location where visitors can meet and interact with a specified character or representative.
-
C.
meetsAs
Indicates that two entities encounter or come together at the same place and time, typically in a planned or recognized interaction.
-
D.
meetsBy
Indicates that one entity encounters or comes together with another entity, typically at a specific time or place.
-
E.
presentsAt
Indicates that one entity delivers or gives a presentation, talk, or performance at a particular event, venue, or occasion.
- 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_69b3454f739481909ff6c28331f0c0b9 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35262649c8190a724c9835cb7ece6 |
completed | March 12, 2026, 11:55 p.m. |
| PD | Predicate disambiguation | batch_69b34f557fe8819085032bf7f0cea5dc |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:18 p.m.