Triple

T24580477
Position Surface form Disambiguated ID Type / Status
Subject Stade Gilbert Brutus E608232 entity
Predicate publicTransit P1288 FINISHED
Object Perpignan public bus network
The Perpignan public bus network is the urban and suburban bus system serving the city of Perpignan and its surrounding area in southern France.
 E1640471 NE 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: Perpignan public bus network | Statement: [Stade Gilbert Brutus, publicTransit, Perpignan public bus network]
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: Perpignan public bus network
Triple: [Stade Gilbert Brutus, publicTransit, Perpignan public bus network]
Generated description
The Perpignan public bus network is the urban and suburban bus system serving the city of Perpignan and its surrounding area in southern France.

Provenance (5 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_69e2c4ce89248190ad99e18f0638dfbb completed April 17, 2026, 11:39 p.m.
NER Named-entity recognition batch_69f2a97fde9c81909d8de91b6358a015 completed April 30, 2026, 12:59 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0ff8774d64819097e290a3d578d840 completed May 22, 2026, 6:32 a.m.
NEDg Description generation batch_6a0ff956f6e48190950c5bace85c9669 completed May 22, 2026, 6:36 a.m.
NED2 Entity disambiguation (via description) batch_6a0ffa00b57081909bc69474734fcb20 completed May 22, 2026, 6:38 a.m.
Created at: April 18, 2026, 2:29 a.m.