Triple

T31253589
Position Surface form Disambiguated ID Type / Status
Subject Cour Saint-Émilion (Paris Métro) E796897 entity
Predicate hasEntranceFrom P1985 FINISHED
Object Cour Saint-Émilion street
Cour Saint-Émilion street is a pedestrian-friendly thoroughfare in Paris’s Bercy district, known for its shops, restaurants, and proximity to the former wine warehouses of Bercy Village.
E1966227 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: Cour Saint-Émilion street | Statement: [Cour Saint-Émilion (Paris Métro), hasEntranceFrom, Cour Saint-Émilion street]
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: Cour Saint-Émilion street
Triple: [Cour Saint-Émilion (Paris Métro), hasEntranceFrom, Cour Saint-Émilion street]
Generated description
Cour Saint-Émilion street is a pedestrian-friendly thoroughfare in Paris’s Bercy district, known for its shops, restaurants, and proximity to the former wine warehouses of Bercy Village.

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_69f224dc84d0819081f1cb6f9127e6b1 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f69d5a343881908d03cd53ce9bbc90 completed May 3, 2026, 12:56 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2b143a8ee0819084125274f3882909 completed June 11, 2026, 8:02 p.m.
NEDg Description generation batch_6a2b18ab550c8190a49bc771916e0936 completed June 11, 2026, 8:20 p.m.
NED2 Entity disambiguation (via description) batch_6a2b19c0e698819087ea1ece619a3482 completed June 11, 2026, 8:25 p.m.
Created at: April 29, 2026, 9:12 p.m.