Triple
T4733120
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | JR Namba Station |
E105057
|
entity |
| Predicate | stationNumberingIntroduced |
P44484
|
FINISHED |
| Object | 2018 |
—
|
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: 2018 | Statement: [JR Namba Station, stationNumberingIntroduced, 2018]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: stationNumberingIntroduced Context triple: [JR Namba Station, stationNumberingIntroduced, 2018]
-
A.
hasRailwayStationNumberingSystem
chosen
Indicates that a railway station is associated with a specific system for assigning it an identifying number or code.
-
B.
stationNumber
Indicates the specific station identifier or code assigned to an entity within a system or network.
-
C.
railwayLineNumber
Indicates the identifying number assigned to a specific railway line within a rail network.
-
D.
beganOfficialNumberingWith
Indicates that an entity started its formal or official numbering sequence with a specified initial number or identifier.
-
E.
hasRailwayLineNumber
Indicates the specific identification number assigned to a railway line associated with an entity.
- 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_69bd43ee52048190b81a4f066534ffb3 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6466354481908595f5bb56025cdb |
completed | March 20, 2026, 3:14 p.m. |
| PD | Predicate disambiguation | batch_69bd6221c3b881908604f35f8de6f16b |
completed | March 20, 2026, 3:05 p.m. |
Created at: March 20, 2026, 1:19 p.m.