Triple

T31754582

Position	Surface form	Disambiguated ID	Type / Status
Subject	Mexico Pavilion at EPCOT	`E810523`	entity
Predicate	hasKiosk	`P49756`	FINISHED
Object	Choza de Margarita Choza de Margarita is an outdoor margarita and snack stand located in the Mexico Pavilion at EPCOT in Walt Disney World.	`E1976183`	NE FINISHED

How this triple was built (3 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]

NEDg Description generation gpt-5.1

Instruction

Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"

Input

Entity: Choza de Margarita
Triple: [Mexico Pavilion at EPCOT, hasKiosk, Choza de Margarita]

Generated description

Choza de Margarita is an outdoor margarita and snack stand located in the Mexico Pavilion at EPCOT in Walt Disney World.

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 (6 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.
NED1	Entity disambiguation (via context triple)	`batch_6a2b948ac1d08190ae42c975a6e9a58c`	completed	June 12, 2026, 5:09 a.m.
NEDg	Description generation	`batch_6a2b96a258e08190878811121d63504c`	completed	June 12, 2026, 5:18 a.m.
NED2	Entity disambiguation (via description)	`batch_6a2b9794d14081909374e7d0c582f4e0`	completed	June 12, 2026, 5:22 a.m.
PD	Predicate disambiguation	`batch_69f6aa20a1588190a53533fc9764efb2`	completed	May 3, 2026, 1:51 a.m.

Created at: April 30, 2026, 11:29 p.m.