Triple
T978140
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Osakako Station |
E21103
|
entity |
| Predicate | hasStationNumbering |
P1289
|
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: [Osakako Station, hasStationNumbering, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStationNumbering Context triple: [Osakako Station, hasStationNumbering, yes]
-
A.
hasJunctionNumbering
Indicates that a road or route is assigned a specific numbering system for its junctions or intersections.
-
B.
hasStationCode
chosen
Indicates that an entity is associated with a specific station identification code.
-
C.
numberOfStations
Indicates the total count of stations associated with or contained by a given entity.
-
D.
hasCategoryNumbering
Indicates that an entity is assigned or associated with a specific category-based numbering or index within a classification system.
-
E.
frontNumbering
Indicates that an entity is assigned a numbering or label that appears at the front or beginning of something (e.g., a sequence, document, or list).
- 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_69a493c2b62c8190b616351789ec47f8 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b47861808190be56a7bbd926e658 |
completed | March 1, 2026, 9:49 p.m. |
| PD | Predicate disambiguation | batch_69a4b2a8a3b08190b4538e119b13f7f5 |
completed | March 1, 2026, 9:42 p.m. |
Created at: March 1, 2026, 7:40 p.m.