Triple

T35767258
Position Surface form Disambiguated ID Type / Status
Subject Paris 13th arrondissement E1034050 entity
Predicate contains P35 FINISHED
Object Gobelins district
The Gobelins district is a historic Parisian neighborhood best known for the Manufacture des Gobelins tapestry factory and its long association with textile production and decorative arts.
E2155051 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: Gobelins district | Statement: [Paris 13th arrondissement, contains, Gobelins district]
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: Gobelins district
Triple: [Paris 13th arrondissement, contains, Gobelins district]
Generated description
The Gobelins district is a historic Parisian neighborhood best known for the Manufacture des Gobelins tapestry factory and its long association with textile production and decorative arts.

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_69f76e13edd081909101629aa829c4ad completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f7a1c8ddc881909696006612f2a8c4 completed May 3, 2026, 7:28 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3885fffdb481908b339b21fd35bddf completed June 22, 2026, 12:46 a.m.
NEDg Description generation batch_6a3886de289881909bbf9a97aae181d4 completed June 22, 2026, 12:50 a.m.
NED2 Entity disambiguation (via description) batch_6a3887d8cec0819093e671703bfd1fca completed June 22, 2026, 12:54 a.m.
Created at: May 3, 2026, 4:06 p.m.