Triple

T13251150
Position Surface form Disambiguated ID Type / Status
Subject M1 line E315531 entity
Predicate hasStation P35 FINISHED
Object Batıkent station E1028578 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: Batıkent station | Statement: [M1 line, hasStation, Batıkent station]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Batıkent station
Context triple: [M1 line, hasStation, Batıkent station]
  • A. Batıkent station chosen
    Batıkent station is a major Ankara Metro station serving the Batıkent district and functioning as a key hub in the city's rapid transit network.
  • B. Konak station
    Konak station is a central underground stop on the İzmir Metro system, serving as one of the main transit hubs in the heart of İzmir, Turkey.
  • C. Kazlıçeşme station
    Kazlıçeşme station is a major railway and commuter rail stop in Istanbul that serves as one of the key terminals on the Marmaray cross-Bosphorus rail system.
  • D. Fahrettin Altay station
    Fahrettin Altay station is a major western terminus and transfer hub on the İzmir Metro system in İzmir, Turkey.
  • E. Medinaceli railway station
    Medinaceli railway station is a regional train station in Medinaceli, Spain, serving as a stop on key rail routes connecting central and northern parts of the country.
  • 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_69d806b1072881909e46bd212259c5f0 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d98f73423c8190932a9edac56df383 completed April 11, 2026, 12:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69f71f17ba9081909929201be937c2cf completed May 3, 2026, 10:10 a.m.
Created at: April 9, 2026, 9:24 p.m.