Triple

T11192746
Position Surface form Disambiguated ID Type / Status
Subject Fanfare of Love E264840 entity
Predicate musicBy P1952 FINISHED
Object Paul Misraki E747585 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: Paul Misraki | Statement: [Fanfare of Love, musicBy, Paul Misraki]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Paul Misraki
Context triple: [Fanfare of Love, musicBy, Paul Misraki]
  • A. Paul Misraki chosen
    Paul Misraki was a French composer best known for his prolific film scores and popular songs in mid-20th-century European cinema.
  • B. Arthur Sadoun
    Arthur Sadoun is a French advertising executive and the chief executive of Publicis Groupe, one of the world’s largest communications and marketing services companies.
  • C. Adnan Kassar
    Adnan Kassar is a prominent Lebanese businessman and banker known for his leadership in international commerce and significant contributions to economic development and education in Lebanon.
  • D. Joe Mimran
    Joe Mimran is a Canadian fashion designer and entrepreneur best known for creating influential lifestyle brands such as Club Monaco and Joe Fresh.
  • E. Henry Barakat
    Henry Barakat was a prominent Egyptian film director and one of the leading figures of classical Egyptian cinema.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d6aa9eb9248190b20211772621b4bc completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e8be025481909d311b587418dfb2 completed April 9, 2026, 5:58 p.m.
NED1 Entity disambiguation (via context triple) batch_69e483f8ecf4819086f0bab3ca9ddcb4 completed April 19, 2026, 7:27 a.m.
Created at: April 8, 2026, 9:29 p.m.