Triple

T29748284
Position Surface form Disambiguated ID Type / Status
Subject Integrated with Fil Bleu bus network E752819 entity
Predicate appliesTo P1129 FINISHED
Object Fil Bleu bus network
Fil Bleu bus network is the public transportation system serving Tours and its surrounding area in France, operating a network of buses and related transit services.
 E1882100 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: Fil Bleu bus network | Statement: [Integrated with Fil Bleu bus network, appliesTo, Fil Bleu 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: Fil Bleu bus network
Triple: [Integrated with Fil Bleu bus network, appliesTo, Fil Bleu bus network]
Generated description
Fil Bleu bus network is the public transportation system serving Tours and its surrounding area in France, operating a network of buses and related transit services.

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_69f0d62c84cc8190846f80ae04fdf8ec completed April 28, 2026, 3:45 p.m.
NER Named-entity recognition batch_69f67368906c81908fbdad28b9fd300d completed May 2, 2026, 9:58 p.m.
NED1 Entity disambiguation (via context triple) batch_6a26aa93193481908686f065e3ec8419 completed June 8, 2026, 11:42 a.m.
NEDg Description generation batch_6a26b05b3408819098b819b46e4df625 completed June 8, 2026, 12:06 p.m.
NED2 Entity disambiguation (via description) batch_6a26b478c450819090c8830333fe1030 completed June 8, 2026, 12:24 p.m.
Created at: April 28, 2026, 7:52 p.m.