Triple

T15444407
Position Surface form Disambiguated ID Type / Status
Subject Brown Leghorn E369988 entity
Predicate recognizedVarietyOf P41829 FINISHED
Object Leghorn E337325 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: Leghorn | Statement: [Brown Leghorn, recognizedVarietyOf, Leghorn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Leghorn
Context triple: [Brown Leghorn, recognizedVarietyOf, Leghorn]
  • A. Leghorn chosen
    Leghorn is the traditional English name for the Italian port city of Livorno on the western coast of Tuscany.
  • B. Genoa
    Genoa is a historic port city in northwestern Italy known for its significant maritime heritage, trade, and role as a major economic hub on the Ligurian coast.
  • C. Genoa
    Genoa is the codename for AMD’s fourth-generation EPYC server processors based on the Zen 4 architecture and the SP5 platform.
  • D. Otranto
    Otranto is a historic coastal town in southern Italy’s Apulia region, known for its medieval castle, cathedral, and strategic position on the Adriatic Sea.
  • E. Génova
    Génova is a small municipality in Colombia’s Quindío Department, known for its coffee-growing traditions and Andean rural landscapes.
  • 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_69d85a19180081909925012fbf4e62a3 completed April 10, 2026, 2:02 a.m.
NER Named-entity recognition batch_69e03ef666e08190a02a01a676306ab9 completed April 16, 2026, 1:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff21ab80288190a1a4df8b714bba66 completed May 9, 2026, 11:59 a.m.
Created at: April 10, 2026, 3:21 a.m.