Triple

T16978037
Position Surface form Disambiguated ID Type / Status
Subject Osu antique market E411864 entity
Predicate locatedIn P40 FINISHED
Object Osu district E1175524 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: Osu district | Statement: [Osu antique market, locatedIn, Osu district]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Osu district
Context triple: [Osu antique market, locatedIn, Osu district]
  • A. Osu district chosen
    Osu district is a lively neighborhood in Accra, Ghana, known for its bustling nightlife, shopping, and cultural events.
  • B. Lavasanat District
    Lavasanat District is an administrative subdivision in northern Tehran Province, Iran, known for its mountainous terrain, cooler climate, and role as a recreational and residential area near the capital.
  • C. Kuta District
    Kuta District is a popular coastal area in southern Bali, Indonesia, known for its busy tourist beaches, nightlife, and surf culture.
  • D. Buka District
    Buka District is an administrative district located within the Tashkent Region of Uzbekistan.
  • E. Eruh District
    Eruh District is an administrative district in southeastern Turkey known for its mountainous terrain and predominantly Kurdish population within Siirt Province.
  • 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_69d886ca8f348190812768ea8d5055ce completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3d185a9408190a991bf8a1ef694f0 completed April 18, 2026, 6:46 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00d477f7ec81909f1f0243004c9050 completed May 10, 2026, 6:54 p.m.
Created at: April 10, 2026, 5:32 a.m.