Triple

T33150998

Position	Surface form	Disambiguated ID	Type / Status
Subject	Raststätte Wetterau	`E848434`	entity
Predicate	cateringOperator	`P149181`	FINISHED
Object	Autogrill (possible) Autogrill is a multinational company specializing in food and beverage services for travelers, primarily operating restaurants and catering outlets along highways, in airports, and at other transit locations.	`E2037344`	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: Autogrill (possible) | Statement: [Raststätte Wetterau, cateringOperator, Autogrill (possible)]

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: Autogrill (possible)
Triple: [Raststätte Wetterau, cateringOperator, Autogrill (possible)]

Generated description

Autogrill is a multinational company specializing in food and beverage services for travelers, primarily operating restaurants and catering outlets along highways, in airports, and at other transit locations.

PD Predicate disambiguation gpt-5-mini-2025-08-07

Target predicate: cateringOperator
Context triple: [Raststätte Wetterau, cateringOperator, Autogrill (possible)]

A. restaurantOperator
Indicates that one entity operates, manages, or runs a restaurant business associated with another entity.
B. operatedDiningService chosen
Indicates that one entity managed and provided dining or food service operations for another entity or location.
C. hasCateringServices
Indicates that an entity provides or offers catering services, such as preparing and supplying food and beverages for events or clients.
D. cateringFocus
Indicates that an entity’s primary business or operational emphasis is on providing catering services.
E. venueOperator
Indicates that one entity operates, manages, or runs a particular venue or event location for another entity.
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_69f3495a458c8190a1d34b237ba0be3f`	completed	April 30, 2026, 12:21 p.m.
NER	Named-entity recognition	`batch_69f6db6af1d88190989810182354d60f`	completed	May 3, 2026, 5:21 a.m.
NED1	Entity disambiguation (via context triple)	`batch_6a35162d7b448190bd25ef02bcf30722`	completed	June 19, 2026, 10:13 a.m.
NEDg	Description generation	`batch_6a3516c7372481908cb6702dcce293ca`	completed	June 19, 2026, 10:15 a.m.
NED2	Entity disambiguation (via description)	`batch_6a35176292108190ad37fb5d57f77636`	completed	June 19, 2026, 10:18 a.m.
PD	Predicate disambiguation	`batch_69f6d82d068c8190940a3200ed760e38`	completed	May 3, 2026, 5:07 a.m.

Created at: May 1, 2026, 1:28 a.m.