Triple
T6764310
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wakoshi Station |
E154677
|
entity |
| Predicate | connectsToArea |
P845
|
FINISHED |
| Object | Saitama Prefecture |
—
|
NE NERFINISHED |
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: Saitama Prefecture | Statement: [Wakoshi Station, connectsToArea, Saitama Prefecture]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: connectsToArea Context triple: [Wakoshi Station, connectsToArea, Saitama Prefecture]
-
A.
connectsArea
Indicates that one area serves as a link or passage between two other areas, enabling movement or interaction between them.
-
B.
connectsTo
chosen
Indicates a relationship where one entity is linked or joined to another, allowing interaction, communication, or transfer between them.
-
C.
connectsLocation
Indicates a relationship where one entity serves as a link or route that joins or provides access between two locations.
-
D.
connectsCommercialAreas
Indicates a relationship where one entity links or provides direct access between two or more commercial areas or business districts.
-
E.
connectsToTouristRegion
Indicates that one entity has a direct linkage or association to a tourist region, such as through location, access, or service provision.
- 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_69c688109c1c8190added9a221292af0 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d327e37081909d576e6eff9eec97 |
completed | March 27, 2026, 6:57 p.m. |
| PD | Predicate disambiguation | batch_69c6d09227108190b253b91967831a85 |
completed | March 27, 2026, 6:46 p.m. |
Created at: March 27, 2026, 2:12 p.m.