Triple

T36366165
Position Surface form Disambiguated ID Type / Status
Subject Lumières de Noël de Montbéliard E895630 entity
Predicate nomLocal P657 FINISHED
Object Lumières de Noël
Lumières de Noël is a renowned Christmas lights festival in Montbéliard, France, celebrated for its elaborate illuminations and festive holiday atmosphere.
 E2179908 NE FINISHED

How this triple was built (3 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: Lumières de Noël | Statement: [Lumières de Noël de Montbéliard, nomLocal, Lumières de Noël]
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: Lumières de Noël
Triple: [Lumières de Noël de Montbéliard, nomLocal, Lumières de Noël]
Generated description
Lumières de Noël is a renowned Christmas lights festival in Montbéliard, France, celebrated for its elaborate illuminations and festive holiday atmosphere.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: nomLocal
Context triple: [Lumières de Noël de Montbéliard, nomLocal, Lumières de Noël]
  • A. officialNameLocal
    Indicates the officially recognized name of an entity as used in the local or native language context.
  • B. addressLocality
    Indicates the city, town, or locality in which an address is situated.
  • C. subdivisionNameLocal
    Indicates the locally used or native-language name assigned to a specific administrative or geographic subdivision.
  • D. nativeLabel chosen
    Indicates the label or name of an entity expressed in its own native or original language.
  • E. nativeCity
    Indicates that a city is the place where a person was born or is originally from.
  • F. None of above.

Provenance (6 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_69f76e5115588190ad8738860b7bc68b completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7bb3ff1b08190802b1063d55d3923 completed May 3, 2026, 9:16 p.m.
NED1 Entity disambiguation (via context triple) batch_6a39a333d8a48190b9dbed51abc10b7b completed June 22, 2026, 9:03 p.m.
NEDg Description generation batch_6a39a49fafe88190ad30192fff3f38e7 completed June 22, 2026, 9:09 p.m.
NED2 Entity disambiguation (via description) batch_6a39a56b41dc81909a3a52a3795cf822 completed June 22, 2026, 9:13 p.m.
PD Predicate disambiguation batch_69f7b9a611a081908dd6aec1df3f4d7f completed May 3, 2026, 9:09 p.m.
Created at: May 3, 2026, 4:10 p.m.