Triple
T9100626
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prospekt Vernadskogo station |
E218140
|
entity |
| Predicate | servesCityDistrict |
P29284
|
FINISHED |
| Object | southwestern Moscow |
—
|
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: southwestern Moscow | Statement: [Prospekt Vernadskogo station, servesCityDistrict, southwestern Moscow]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servesCityDistrict Context triple: [Prospekt Vernadskogo station, servesCityDistrict, southwestern Moscow]
-
A.
targetCityDistrict
Indicates that one entity is a specific city district that serves as the target or destination in relation to another entity.
-
B.
servedDistrict
Indicates that an entity has provided official service or representation to a particular district.
-
C.
cityServedRegion
Indicates that a city provides services to, or functions as an administrative or economic center for, a specified region.
-
D.
servesSuburbsOf
Indicates that a service, route, or facility provides coverage or support to the suburban areas associated with a particular city or region.
-
E.
basedInDistrict
chosen
Indicates that an entity is located or has its primary base of operations within a specific administrative district.
- 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_69ca83d9844081908e561e367fda6d45 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc9711babc8190a336812dd08d9c73 |
completed | April 1, 2026, 3:54 a.m. |
| PD | Predicate disambiguation | batch_69cc65fc7f408190a5846e29ab3b97e5 |
completed | April 1, 2026, 12:25 a.m. |
Created at: March 30, 2026, 7:15 p.m.