Triple

T16281583
Position Surface form Disambiguated ID Type / Status
Subject Francesco Esposito E395274 entity
Predicate componentOfName P5298 FINISHED
Object Francesco E1164514 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: Francesco | Statement: [Francesco Esposito, componentOfName, Francesco]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Francesco
Context triple: [Francesco Esposito, componentOfName, Francesco]
  • A. Francesco
    Francesco is the Italian given name of Frank Nitti, a notorious American mobster and key figure in Al Capone’s Chicago Outfit.
  • B. Francesco
    Francesco is the given name of Italian actor Franco Nero, renowned for his iconic role in the Spaghetti Western film "Django."
  • C. Francesco
    Francesco is the birth name of Frank Capra, the renowned Italian-American film director known for classic Hollywood movies such as "It's a Wonderful Life."
  • D. Francesco chosen
    Francesco is a masculine given name of Italian origin, derived from the Latin Franciscus and commonly associated with figures such as Saint Francis of Assisi.
  • E. Filippo
    Filippo is an Italian given name most famously borne by former professional footballer and manager Filippo Inzaghi.
  • 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_69d87f22c7248190a54c949738441e2e completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e24910c6b881909ae5cc0908dd8eb2 completed April 17, 2026, 2:52 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0025fb88488190a2979c75fba67b9d completed May 10, 2026, 6:30 a.m.
Created at: April 10, 2026, 5:05 a.m.