Triple
T15777638
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baku Metro |
E382528
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Icherisheher station
Icherisheher station is a metro station on the Baku Metro system serving the historic Old City area of Baku, Azerbaijan.
|
E1181266
|
NE FINISHED |
How this triple was built (4 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: Icherisheher station | Statement: [Baku Metro, hasStation, Icherisheher station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Icherisheher station Context triple: [Baku Metro, hasStation, Icherisheher station]
-
A.
Beruniy station
Beruniy station is a metro station in Tashkent, Uzbekistan, serving as part of the city's Tashkent Metro rapid transit system.
-
B.
Frunzenskaya station
Frunzenskaya station is a Moscow Metro station known for its deep-level construction and classic Soviet-era architectural design.
-
C.
Yelshanka station
Yelshanka station is a stop on the Volgograd Metrotram light rail system in Volgograd, Russia.
-
D.
Khimvolokno station
Khimvolokno station is a stop on the Volgograd Metrotram light rail system in Volgograd, Russia.
-
E.
Obelya station
Obelya station is a metro station in Sofia, Bulgaria, serving as an interchange point between lines of the Sofia Metro network.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Icherisheher station Triple: [Baku Metro, hasStation, Icherisheher station]
Generated description
Icherisheher station is a metro station on the Baku Metro system serving the historic Old City area of Baku, Azerbaijan.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Icherisheher station Target entity description: Icherisheher station is a metro station on the Baku Metro system serving the historic Old City area of Baku, Azerbaijan.
-
A.
Beruniy station
Beruniy station is a metro station in Tashkent, Uzbekistan, serving as part of the city's Tashkent Metro rapid transit system.
-
B.
Frunzenskaya station
Frunzenskaya station is a Moscow Metro station known for its deep-level construction and classic Soviet-era architectural design.
-
C.
Yelshanka station
Yelshanka station is a stop on the Volgograd Metrotram light rail system in Volgograd, Russia.
-
D.
Khimvolokno station
Khimvolokno station is a stop on the Volgograd Metrotram light rail system in Volgograd, Russia.
-
E.
Obelya station
Obelya station is a metro station in Sofia, Bulgaria, serving as an interchange point between lines of the Sofia Metro network.
- F. None of above. chosen
Provenance (5 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_69d86da09a10819082fe9797b23e4664 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e05199cd8881909462462cec34d35a |
completed | April 16, 2026, 3:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffa9361f5c8190b68702154d05bbc2 |
completed | May 9, 2026, 9:37 p.m. |
| NEDg | Description generation | batch_69ffaa3903408190b7beaa6b461bd2bd |
completed | May 9, 2026, 9:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffab0c79d4819085f0ed6a4edcb7fb |
completed | May 9, 2026, 9:45 p.m. |
Created at: April 10, 2026, 4:47 a.m.