Triple

T14336138
Position Surface form Disambiguated ID Type / Status
Subject Nobu restaurants E355470 entity
Predicate hasLocation P40 FINISHED
Object Ibiza E23828 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: Ibiza | Statement: [Nobu restaurants, hasLocation, Ibiza]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ibiza
Context triple: [Nobu restaurants, hasLocation, Ibiza]
  • A. Mallorca
    Mallorca is the largest of Spain’s Balearic Islands, renowned for its Mediterranean beaches, rugged limestone mountains, and historic towns such as Palma.
  • B. Formentera
    Formentera is a small Balearic Island in the Mediterranean Sea, renowned for its pristine white-sand beaches, crystal-clear waters, and laid-back atmosphere.
  • C. Ibiza, Spain chosen
    Ibiza, Spain is a Mediterranean island renowned for its vibrant nightlife, electronic music scene, and picturesque beaches.
  • D. Minorca
    Minorca is one of Spain’s Balearic Islands in the Mediterranean Sea, known for its natural harbors, beaches, and historical strategic importance.
  • E. Javea
    Javea is a coastal town on Spain’s Costa Blanca known for its Mediterranean beaches, historic old town, and popular holiday tourism.
  • 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_69d8278fa2108190bc0d0e7939c1eb03 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de8c2241e48190a0c626b3d741966a completed April 14, 2026, 6:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd5508da0c8190a8ea44ca737cf352 completed May 8, 2026, 3:14 a.m.
Created at: April 10, 2026, 1:14 a.m.