Triple

T1128617
Position Surface form Disambiguated ID Type / Status
Subject Tyrrhenian Sea E24776 entity
Predicate hasPortCity P2745 FINISHED
Object Livorno E67624 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: Livorno | Statement: [Tyrrhenian Sea, hasPortCity, Livorno]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Livorno
Context triple: [Tyrrhenian Sea, hasPortCity, Livorno]
  • A. Livorno chosen
    Livorno is a port city on Italy’s western coast, historically notable for its diverse communities and significant Jewish population.
  • B. Viareggio
    Viareggio is a coastal city in Tuscany, Italy, renowned for its seaside resorts and famous annual Carnival.
  • C. 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.
  • D. Messina
    Messina is a major port city in northeastern Sicily, Italy, located on the Strait of Messina opposite mainland Calabria.
  • E. Cagliari
    Cagliari is the capital city of the Italian island of Sardinia, known for its historic architecture, Mediterranean harbor, and role as a key political and cultural center in the region.
  • 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_69a4940712c88190aa244f3fc6070a65 completed March 1, 2026, 7:31 p.m.
NER Named-entity recognition batch_69a4bbdea9b88190a88da718bf5c1897 completed March 1, 2026, 10:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69ace54f52988190b25c35271721c3ee completed March 8, 2026, 2:56 a.m.
Created at: March 1, 2026, 7:44 p.m.