Triple

T3776039
Position Surface form Disambiguated ID Type / Status
Subject Turkish Air Force E83310 entity
Predicate headquartersLocation P62 FINISHED
Object Etimesgut, Ankara E322913 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: Etimesgut, Ankara | Statement: [Turkish Air Force, headquartersLocation, Etimesgut, Ankara]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Etimesgut, Ankara
Context triple: [Turkish Air Force, headquartersLocation, Etimesgut, Ankara]
  • A. Etimesgut chosen
    Etimesgut is a rapidly growing suburban district and municipality on the western side of Ankara, Turkey’s capital city.
  • B. Çankaya
    Çankaya is a central district of Ankara, Turkey, known for housing key government institutions, foreign embassies, and major national landmarks.
  • C. Yenimahalle
    Yenimahalle is a major district of Ankara, Turkey, known for hosting key government institutions and residential areas within the capital.
  • D. Ataşehir
    Ataşehir is a modern residential and business district on the Asian side of Istanbul, known for its high-rise developments and financial centers.
  • E. Sultanbeyli
    Sultanbeyli is a densely populated, predominantly residential district on the Asian side of Istanbul, known for its rapid urbanization and working-class character.
  • 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_69ad8b235e608190b5a2b1d1bfcef50b completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69adcc5be3c48190a72e840d8214bb74 completed March 8, 2026, 7:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4e53209888190823412fabacbc914 completed March 14, 2026, 4:33 a.m.
Created at: March 8, 2026, 3:36 p.m.