Triple
T5553202
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 湘南新宿ライン |
E145575
|
entity |
| Predicate | 接続エリア |
P24718
|
FINISHED |
| Object | 神奈川県湘南エリア |
—
|
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: 神奈川県湘南エリア | Statement: [湘南新宿ライン, 接続エリア, 神奈川県湘南エリア]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 接続エリア Context triple: [湘南新宿ライン, 接続エリア, 神奈川県湘南エリア]
-
A.
connectsArea
Indicates that one area serves as a link or passage between two other areas, enabling movement or interaction between them.
-
B.
connectsLocation
Indicates a relationship where one entity serves as a link or route that joins or provides access between two locations.
-
C.
connectivity
Indicates...the existence, degree, or quality of a link or communication pathway between entities, allowing interaction or information flow between them.
-
D.
networkArea
Indicates that one entity defines, covers, or is responsible for a specific network area associated with another entity.
-
E.
connectsRegions
chosen
Indicates a relationship where one entity links or joins two or more distinct regions, enabling passage, interaction, or continuity between them.
- 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_69c008fb879c81909f5bfa56fadc1d46 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01ff9c9c48190b5e587d58c6515d8 |
completed | March 22, 2026, 4:59 p.m. |
| PD | Predicate disambiguation | batch_69c01b0e72f08190bf705d8fe1639401 |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:35 p.m.