Triple

T32466814
Position Surface form Disambiguated ID Type / Status
Subject Le Plaisir E829731 entity
Predicate musicBy P1952 FINISHED
Object Joe Hajos
Joe Hajos was a film composer known for scoring European films in the mid-20th century, including Max Ophüls’ anthology film "Le Plaisir."
E2014346 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: Joe Hajos | Statement: [Le Plaisir, musicBy, Joe Hajos]
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: Joe Hajos
Triple: [Le Plaisir, musicBy, Joe Hajos]
Generated description
Joe Hajos was a film composer known for scoring European films in the mid-20th century, including Max Ophüls’ anthology film "Le Plaisir."

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_69f3491ee87c81908cbf5890079c2af6 completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6c35217548190a7a5df687aeac236 completed May 3, 2026, 3:38 a.m.
NED1 Entity disambiguation (via context triple) batch_6a3485f7cdc88190b653f8a7e286a4f1 completed June 18, 2026, 11:57 p.m.
NEDg Description generation batch_6a3486a95ecc8190b58597914c9a809c completed June 19, 2026, midnight
NED2 Entity disambiguation (via description) batch_6a348767eeb08190b80bf696b49d1f21 completed June 19, 2026, 12:03 a.m.
Created at: May 1, 2026, 12:57 a.m.