Triple

T36888457
Position Surface form Disambiguated ID Type / Status
Subject The Death of Louis XIV E911676 entity
Predicate writer P1360 FINISHED
Object Thierry Lounas
Thierry Lounas is a French film producer and screenwriter known for his work on art-house and auteur cinema, including co-writing the historical drama "The Death of Louis XIV."
E2286631 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: Thierry Lounas | Statement: [The Death of Louis XIV, writer, Thierry Lounas]
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: Thierry Lounas
Triple: [The Death of Louis XIV, writer, Thierry Lounas]
Generated description
Thierry Lounas is a French film producer and screenwriter known for his work on art-house and auteur cinema, including co-writing the historical drama "The Death of Louis XIV."

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_69f76e8335908190b77e7e11d0e80820 completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69f9fd70b0a88190baabcee7ab6c217e completed May 5, 2026, 2:23 p.m.
NED1 Entity disambiguation (via context triple) batch_6a46c8f009dc81909c357d12f2230535 completed July 2, 2026, 8:24 p.m.
NEDg Description generation batch_6a46c9bf3ca481908c9200bfd33b4449 completed July 2, 2026, 8:27 p.m.
NED2 Entity disambiguation (via description) batch_6a46ca1a1c508190a922cbc6c1dea228 completed July 2, 2026, 8:29 p.m.
Created at: May 3, 2026, 4:13 p.m.