Triple

T31122062
Position Surface form Disambiguated ID Type / Status
Subject Archevêché de Paris E793247 entity
Predicate previousOrdinary P196328 FINISHED
Object Michel Aupetit
Michel Aupetit is a French Roman Catholic prelate and former Archbishop of Paris, known for his background as a medical doctor before entering the priesthood.
 E2292205 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: Michel Aupetit | Statement: [Archevêché de Paris, previousOrdinary, Michel Aupetit]
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: Michel Aupetit
Triple: [Archevêché de Paris, previousOrdinary, Michel Aupetit]
Generated description
Michel Aupetit is a French Roman Catholic prelate and former Archbishop of Paris, known for his background as a medical doctor before entering the priesthood.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: previousOrdinary
Context triple: [Archevêché de Paris, previousOrdinary, Michel Aupetit]
  • A. pastOrdinary
    Indicates that the related action or state occurred in the past using the simple (non-progressive, non-perfect) tense.
  • B. currentOrdinary
    Indicates that an entity currently holds or is in the state of an ordinary (non-special or non-exceptional) status or role.
  • C. firstOrdinary
    Indicates that the subject is the first entity to hold or occupy an ordinary (non-special, standard) position, role, or status in a given sequence or context.
  • D. usesOrdinary
    Indicates that one entity makes use of another entity in a standard, non-specialized, or typical manner.
  • E. ordinary
    Indicates that something or someone is typical, usual, or not special or exceptional in the relevant context.
  • F. None of above. chosen

Provenance (7 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_69f224d0a7688190af3fe3e6e26d01ed completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69fe21b0cba48190b56c39e9f1c0eafa completed May 8, 2026, 5:47 p.m.
NED1 Entity disambiguation (via context triple) batch_6a5cd1fe4a088190831c50d60d4cbc9c completed July 19, 2026, 1:32 p.m.
NEDg Description generation batch_6a5cd2c479188190ad94a273b5d6c4bd completed July 19, 2026, 1:36 p.m.
NED2 Entity disambiguation (via description) batch_6a5cd3dea28c8190a037c66f1ea7fc16 completed July 19, 2026, 1:40 p.m.
PD Predicate disambiguation batch_69fe204576848190aecf204e2adba5dc completed May 8, 2026, 5:41 p.m.
PDg Predicate description generation batch_69fe21afdc4c8190913ac4b55a9a5f52 completed May 8, 2026, 5:47 p.m.
Created at: April 29, 2026, 9:04 p.m.