Triple

T534983
Position Surface form Disambiguated ID Type / Status
Subject Haifa E12305 entity
Predicate hasSisterCity P919 FINISHED
Object Marseille E15143 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: Marseille | Statement: [Haifa, hasSisterCity, Marseille]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Marseille
Context triple: [Haifa, hasSisterCity, Marseille]
  • A. Marseille chosen
    Marseille is a historic Mediterranean port city in southern France known for its diverse culture, maritime heritage, and role as a major economic hub.
  • B. Lyon
    Lyon is a major city in east-central France known for its historical and architectural landmarks, gastronomy, and role as a key economic and cultural center.
  • C. Toulon
    Toulon is a major port city on France’s Mediterranean coast that serves as the principal base of the French Navy.
  • D. Toulouse
    Toulouse is a major city in southwestern France known for its aerospace industry, historic pink-brick architecture, and vibrant university and cultural life.
  • E. Ajaccio
    Ajaccio is a coastal city on the French island of Corsica, best known as the birthplace of Napoleon Bonaparte and now a popular Mediterranean tourist destination.
  • 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_69a4933208e88190891f5debab1b776d completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a496d904408190a2ed16c839017623 completed March 1, 2026, 7:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac427687e88190aa152ffddf89d56c completed March 7, 2026, 3:21 p.m.
Created at: March 1, 2026, 7:32 p.m.