Triple

T33074852
Position Surface form Disambiguated ID Type / Status
Subject Havre–Caumartin (Paris Métro) E846330 entity
Predicate partOfInterchangeComplexWith P84508 FINISHED
Object Opéra (Paris Métro) station
Opéra (Paris Métro) station is a major underground hub in central Paris serving multiple metro lines and providing direct connections to nearby stations and the Palais Garnier opera house.
E2058429 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: Opéra (Paris Métro) station | Statement: [Havre–Caumartin (Paris Métro), partOfInterchangeComplexWith, Opéra (Paris Métro) 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: Opéra (Paris Métro) station
Triple: [Havre–Caumartin (Paris Métro), partOfInterchangeComplexWith, Opéra (Paris Métro) station]
Generated description
Opéra (Paris Métro) station is a major underground hub in central Paris serving multiple metro lines and providing direct connections to nearby stations and the Palais Garnier opera house.

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_69f3495405b88190967af2157b43b896 completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69ffdd7706b88190870046670d46b397 completed May 10, 2026, 1:20 a.m.
NED1 Entity disambiguation (via context triple) batch_6a35afb4ab708190a725bce057719f1b completed June 19, 2026, 9:08 p.m.
NEDg Description generation batch_6a35b12f787c8190978b95d0105cebbd completed June 19, 2026, 9:14 p.m.
NED2 Entity disambiguation (via description) batch_6a35b1a8450c81909cdf93e9a4973784 completed June 19, 2026, 9:16 p.m.
Created at: May 1, 2026, 1:25 a.m.