Triple
T9901467
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chernyshevskaya metro station |
E182291
|
entity |
| Predicate | hasUndergroundHall |
P84284
|
FINISHED |
| Object | yes |
—
|
LITERAL 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: yes | Statement: [Chernyshevskaya metro station, hasUndergroundHall, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasUndergroundHall Context triple: [Chernyshevskaya metro station, hasUndergroundHall, yes]
-
A.
hasUndergroundFacilities
Indicates that one entity possesses or contains facilities or infrastructure located below ground level in relation to another entity.
-
B.
hasUndergroundSection
Indicates that an entity includes a portion or segment that is located below ground level.
-
C.
hasUndergroundVestibule
chosen
Indicates that one entity possesses or includes an underground vestibule space connected to it.
-
D.
hasUndergroundDepth
Indicates that one entity has a specified vertical extent or depth located below the ground surface relative to another reference or context.
-
E.
hasStationHall
Indicates that one entity (typically a station) includes or is associated with a station hall area as part of its structure or facilities.
- F. None of above.
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_69ca82876f8081909cf75df0f99bb13f |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cdb4e221448190b536742d1269f9d7 |
completed | April 2, 2026, 12:14 a.m. |
| PD | Predicate disambiguation | batch_69cd1d8c584081908b73de75eb18e438 |
completed | April 1, 2026, 1:28 p.m. |
Created at: March 30, 2026, 8:40 p.m.