Triple

T2986449
Position Surface form Disambiguated ID Type / Status
Subject Government of Turkey E80636 entity
Predicate seat P75 FINISHED
Object Ankara E11226 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: Ankara | Statement: [Government of Turkey, seat, Ankara]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ankara
Context triple: [Government of Turkey, seat, Ankara]
  • A. Ankara chosen
    Ankara is the political and administrative center of Turkey, known for hosting the country’s government institutions and foreign embassies.
  • B. Kayseri
    Kayseri is a historic city in central Turkey, known for its Seljuk and Ottoman architectural heritage and its role as a major commercial and cultural center in Anatolia.
  • C. Istanbul
    Istanbul is a transcontinental metropolis straddling Europe and Asia, renowned as Turkey’s cultural and economic hub and for its rich history as the former capital of the Byzantine and Ottoman Empires.
  • D. Ankara Metropolitan Municipality
    Ankara Metropolitan Municipality is the primary local government authority responsible for administering and providing public services across Turkey’s capital city, Ankara.
  • E. Samsun
    Samsun is a major Turkish port city on the Black Sea coast, known as an important regional hub for maritime trade and industry.
  • 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_69ad8b16c3488190b47b6aa7a59a335b completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad99c76dfc8190b08bd6110ffabf25 completed March 8, 2026, 3:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69b1f8629c7c8190b597255ab8f391af completed March 11, 2026, 11:18 p.m.
Created at: March 8, 2026, 2:59 p.m.