Triple

T25214643
Position Surface form Disambiguated ID Type / Status
Subject Métropole du Grand Paris E631793 entity
Predicate legalFoundation P125 FINISHED
Object MAPTAM law
The MAPTAM law is a French legislative act that redefined metropolitan governance and territorial organization, notably creating and structuring major metropolitan authorities such as the Métropole du Grand Paris.
E1669488 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: MAPTAM law | Statement: [Métropole du Grand Paris, legalFoundation, MAPTAM law]
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: MAPTAM law
Triple: [Métropole du Grand Paris, legalFoundation, MAPTAM law]
Generated description
The MAPTAM law is a French legislative act that redefined metropolitan governance and territorial organization, notably creating and structuring major metropolitan authorities such as the Métropole du Grand Paris.

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_69e75a8d1aa48190a4320acd3654762c completed April 21, 2026, 11:07 a.m.
NER Named-entity recognition batch_69f47b8bd25c819089de15eac12cd285 completed May 1, 2026, 10:08 a.m.
NED1 Entity disambiguation (via context triple) batch_6a105d3686a88190919ff1d24d11dd31 completed May 22, 2026, 1:42 p.m.
NEDg Description generation batch_6a10610813208190be1babbf77fa1bc9 completed May 22, 2026, 1:58 p.m.
NED2 Entity disambiguation (via description) batch_6a106166290c8190b744e68b8b89362e completed May 22, 2026, 2 p.m.
Created at: April 21, 2026, 12:58 p.m.