Triple
T32201799
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Takebashi Station |
E822556
|
entity |
| Predicate | hasStationOffice |
P198553
|
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: [Takebashi Station, hasStationOffice, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStationOffice Context triple: [Takebashi Station, hasStationOffice, yes]
-
A.
hasStationAt
Indicates that an entity maintains or operates a station located at a specified place.
-
B.
hasStationBuilding
Indicates that a station is associated with or includes a station building as part of its facilities.
-
C.
hasStationHouseLocation
Indicates that a station house is located at or associated with a specific place or geographic location.
-
D.
hasSupportingOffice
Indicates that an entity is associated with or served by a particular office that provides support or administrative services to it.
-
E.
headquartersStation
Indicates that a particular station serves as the main headquarters location for an organization or entity.
- F. None of above. chosen
Provenance (4 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_69f349093174819086e633c190a51aa8 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fef2db323c8190821bda53f22a42be |
completed | May 9, 2026, 8:39 a.m. |
| PD | Predicate disambiguation | batch_69fef21d63c88190abf6a99b59b3c655 |
completed | May 9, 2026, 8:36 a.m. |
| PDg | Predicate description generation | batch_69fef2da7d388190a5712a5094741f18 |
completed | May 9, 2026, 8:39 a.m. |
Created at: May 1, 2026, 12:36 a.m.