Triple
T22358576
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Istanbul commuter rail network |
E552719
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object | Bakırköy station |
—
|
NE NERFINISHED |
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: Bakırköy station | Statement: [Istanbul commuter rail network, hasStation, Bakırköy station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bakırköy station Context triple: [Istanbul commuter rail network, hasStation, Bakırköy station]
-
A.
Bakırköy station
chosen
Bakırköy station is a railway station in Istanbul, Turkey, serving as a stop on the city’s cross-continental commuter rail corridor.
-
B.
Göztepe station
Göztepe station is a railway station in Istanbul, Turkey, serving passengers on the city’s cross-continental Halkalı–Gebze commuter rail corridor.
-
C.
Florya station
Florya station is a railway stop in Istanbul, Turkey, serving passengers on the city’s Marmaray commuter rail network.
-
D.
Bostancı station
Bostancı station is a railway stop in Istanbul, Turkey, serving passengers on the city’s Marmaray commuter rail system.
-
E.
Küçükçekmece station
Küçükçekmece station is a railway stop in Istanbul, Turkey, serving suburban and commuter rail services on the city’s European side.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e4affcc8190ba7c27d29062558d |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f157d1a87c8190a3e195cfbbb0d64f |
completed | April 29, 2026, 12:58 a.m. |
Created at: April 16, 2026, 8:44 p.m.