Triple

T19880699
Position Surface form Disambiguated ID Type / Status
Subject Gare de Boulevard Victor E477761 entity
Predicate locatedNear P294 FINISHED
Object Boulevard Victor
Boulevard Victor is a major Parisian thoroughfare in the 15th arrondissement, known for its proximity to the Boulevard Victor railway station and its role as a key urban artery in the city’s southwest.
E2054624 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: Boulevard Victor | Statement: [Gare de Boulevard Victor, locatedNear, Boulevard Victor]
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: Boulevard Victor
Triple: [Gare de Boulevard Victor, locatedNear, Boulevard Victor]
Generated description
Boulevard Victor is a major Parisian thoroughfare in the 15th arrondissement, known for its proximity to the Boulevard Victor railway station and its role as a key urban artery in the city’s southwest.

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_69d8e51f32b08190b3687f4f60353250 completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e658de4b288190a41bee67f570be1e completed April 20, 2026, 4:48 p.m.
NED1 Entity disambiguation (via context triple) batch_6a359583c21c819083b2c2d5ac15ad11 completed June 19, 2026, 7:16 p.m.
NEDg Description generation batch_6a35a07b378c81909c246bc347c47ee3 completed June 19, 2026, 8:03 p.m.
NED2 Entity disambiguation (via description) batch_6a35a103368881908241e8cc960d871d completed June 19, 2026, 8:05 p.m.
Created at: April 10, 2026, 1:52 p.m.