Triple
T13729753
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dzhankoy |
E329763
|
entity |
| Predicate | hasRailwayStation |
P918
|
FINISHED |
| Object | Dzhankoy-2 railway station |
E1056645
|
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: Dzhankoy-2 railway station | Statement: [Dzhankoy, hasRailwayStation, Dzhankoy-2 railway station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dzhankoy-2 railway station Context triple: [Dzhankoy, hasRailwayStation, Dzhankoy-2 railway station]
-
A.
Dzhankoy railway station
chosen
Dzhankoy railway station is a major rail junction in the town of Dzhankoy in Crimea, serving as an important hub for regional and long-distance train routes.
-
B.
Balkanabat railway station
Balkanabat railway station is the main rail transport hub serving the city of Balkanabat in western Turkmenistan.
-
C.
Kargar station
Kargar station is a metro stop on Tehran’s urban rail network serving passengers along Line 6.
-
D.
Yelshanka station
Yelshanka station is a stop on the Volgograd Metrotram light rail system in Volgograd, Russia.
-
E.
Bytom Railway Station
Bytom Railway Station is a historic rail transport hub in the city of Bytom, Poland, serving as a key node in the region’s passenger and freight railway network.
- 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_69d80772315881908f980cae40d91664 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69de01f746cc8190abde237bbb7e6c78 |
completed | April 14, 2026, 8:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7a84c02e08190b8ef620575157c14 |
completed | May 3, 2026, 7:55 p.m. |
Created at: April 9, 2026, 9:55 p.m.