Triple
T16226649
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Makiochi Station |
E393866
|
entity |
| Predicate | hasAdjacentPrefecture |
P62207
|
FINISHED |
| Object | Hyōgo 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: Hyōgo Prefecture | Statement: [Makiochi Station, hasAdjacentPrefecture, Hyōgo Prefecture]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAdjacentPrefecture Context triple: [Makiochi Station, hasAdjacentPrefecture, Hyōgo Prefecture]
-
A.
hasNearbyPrefecture
Indicates that one administrative region has another prefecture located geographically close to it.
-
B.
hasPrefecture
Indicates that one administrative region or country possesses or is associated with a specific prefecture as a subordinate territorial unit.
-
C.
adjacentProvince
chosen
Indicates that two provinces share a common boundary and are directly next to each other geographically.
-
D.
hasNearbyProvince
Indicates that one province is geographically close to or directly adjacent to another province.
-
E.
accessPrefecture
Indicates that an entity has access to, or is associated with accessing, a specific prefecture.
- 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_69d87f204df88190a8f88923decf9835 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e23d26b02c819080b70ab7cc3bcc24 |
completed | April 17, 2026, 2:01 p.m. |
| PD | Predicate disambiguation | batch_69e219e94a448190b73a4e6aa374eb4a |
completed | April 17, 2026, 11:30 a.m. |
Created at: April 10, 2026, 5:03 a.m.