Triple

T26804490
Position Surface form Disambiguated ID Type / Status
Subject Quartier Notre‑Dame‑des‑Champs E671189 entity
Predicate hasPublicTransport P1288 FINISHED
Object Saint‑Placide station
Saint‑Placide station is a Paris Métro station on Line 4 located in the 6th arrondissement of Paris, France.
 E1750995 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: Saint‑Placide station | Statement: [Quartier Notre‑Dame‑des‑Champs, hasPublicTransport, Saint‑Placide station]
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: Saint‑Placide station
Triple: [Quartier Notre‑Dame‑des‑Champs, hasPublicTransport, Saint‑Placide station]
Generated description
Saint‑Placide station is a Paris Métro station on Line 4 located in the 6th arrondissement of Paris, 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_69eeb31fbd888190a82dac5822e453bc completed April 27, 2026, 12:51 a.m.
NER Named-entity recognition batch_69f61a1b6c348190a54a0ac2a0b463b5 completed May 2, 2026, 3:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1229820f40819099e02b04af0803f3 completed May 23, 2026, 10:26 p.m.
NEDg Description generation batch_6a122a6dd674819088bf5cf55ac55ec5 completed May 23, 2026, 10:30 p.m.
NED2 Entity disambiguation (via description) batch_6a122b4b3c488190b95edec5469dfd71 completed May 23, 2026, 10:33 p.m.
Created at: April 27, 2026, 4:25 a.m.