Triple
T27454874
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tamachi Station |
E692559
|
entity |
| Predicate | hasNearbyCommercialFacilities |
P191961
|
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: [Tamachi Station, hasNearbyCommercialFacilities, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbyCommercialFacilities Context triple: [Tamachi Station, hasNearbyCommercialFacilities, yes]
-
A.
hasNearbyFacility
Indicates that one entity is located close to or in the vicinity of a particular facility.
-
B.
hasConvenienceStore
Indicates that one entity possesses, contains, or is associated with a convenience store.
-
C.
hasMajorCompanyNearby
Indicates that a location or entity is situated close to at least one large or significant company.
-
D.
hasOutletNear
Indicates that one entity has a physical outlet or branch located in close proximity to another specified location or entity.
-
E.
hasNearbyLandUse
Indicates that one land area is located close to another area characterized by a specific type of land use.
- 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_69ef5207903881909427745cda05d27a |
completed | April 27, 2026, 12:09 p.m. |
| NER | Named-entity recognition | batch_69fcf1b3d9a08190850b388308656266 |
completed | May 7, 2026, 8:10 p.m. |
| PD | Predicate disambiguation | batch_69fcf0226d8c8190b23dceafb1794995 |
completed | May 7, 2026, 8:03 p.m. |
| PDg | Predicate description generation | batch_69fcf1b241888190a243f07051c71383 |
completed | May 7, 2026, 8:10 p.m. |
Created at: April 27, 2026, 12:48 p.m.