Triple
T23671611
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tōkaidō post station |
E584743
|
entity |
| Predicate | numberOfStationsOnRoute |
P1301
|
FINISHED |
| Object | 53 |
—
|
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: 53 | Statement: [Tōkaidō post station, numberOfStationsOnRoute, 53]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfStationsOnRoute Context triple: [Tōkaidō post station, numberOfStationsOnRoute, 53]
-
A.
numberOfStations
chosen
Indicates the total count of stations associated with or contained by a given entity.
-
B.
numberOfUndergroundStations
Indicates the total count of underground (subway/metro) stations associated with a given entity.
-
C.
stationNumber
Indicates the specific station identifier or code assigned to an entity within a system or network.
-
D.
stopsAtFewerStationsThan
Indicates that one transit service or route makes stops at a smaller number of stations than another transit service or route.
-
E.
numberOfRoutes
Indicates the total count of distinct routes or paths associated with a given entity or between specified entities.
- 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_69e24901421881908c17a5293bdd4a8e |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1b41002f881908cd744ed44b70f5d |
completed | April 29, 2026, 7:32 a.m. |
| PD | Predicate disambiguation | batch_69f118dd13008190a8799b4e9cadbd79 |
completed | April 28, 2026, 8:30 p.m. |
Created at: April 17, 2026, 6:50 p.m.