Triple
T10079343
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kobe Municipal Subway |
E213857
|
entity |
| Predicate | totalStations |
P1301
|
FINISHED |
| Object | approximately 28 |
—
|
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: approximately 28 | Statement: [Kobe Municipal Subway, totalStations, approximately 28]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: totalStations Context triple: [Kobe Municipal Subway, totalStations, approximately 28]
-
A.
numberOfStations
chosen
Indicates the total count of stations associated with or contained by a given entity.
-
B.
hasStations
Indicates that one entity possesses, contains, or is associated with one or more stations.
-
C.
hasFocalStations
Indicates that an entity is associated with one or more primary or central stations that serve as its main points of focus or operation.
-
D.
stationNumber
Indicates the specific station identifier or code assigned to an entity within a system or network.
-
E.
hasDedicatedStations
Indicates that specific stations are exclusively assigned or reserved for a particular entity or purpose.
- 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_69ca839bf730819086900c323c9b8c95 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cdd031ce748190bb71189afd331979 |
completed | April 2, 2026, 2:10 a.m. |
| PD | Predicate disambiguation | batch_69cd4b97870481908f7a89df10d58a9e |
completed | April 1, 2026, 4:45 p.m. |
Created at: March 30, 2026, 9 p.m.