Triple
T31754582
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mexico Pavilion at EPCOT |
E810523
|
entity |
| Predicate | hasKiosk |
P49756
|
FINISHED |
| Object | Choza de Margarita |
—
|
NE NERFINISHED |
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: Choza de Margarita | Statement: [Mexico Pavilion at EPCOT, hasKiosk, Choza de Margarita]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasKiosk Context triple: [Mexico Pavilion at EPCOT, hasKiosk, Choza de Margarita]
-
A.
hasStationManagerKiosk
Indicates that a station manager operates or is assigned to a specific kiosk.
-
B.
hasRetailKiosks
chosen
Indicates that one entity operates or maintains retail kiosks associated with or located within another entity.
-
C.
hasScreen
Indicates that an entity is equipped with or includes a screen or display component.
-
D.
hasVIPTerminal
Indicates that one entity possesses or provides access to a VIP (very important person) terminal associated with another entity.
-
E.
hasKeyboard
Indicates that one entity possesses or is equipped with a keyboard as a component or accessory.
- 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_69f348e340d48190b780fae618c51464 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6ab7a9a408190a227d66d8c1a55f3 |
completed | May 3, 2026, 1:57 a.m. |
| PD | Predicate disambiguation | batch_69f6aa20a1588190a53533fc9764efb2 |
completed | May 3, 2026, 1:51 a.m. |
Created at: April 30, 2026, 11:29 p.m.