Triple
T14442518
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Temple 6: Anrakuji |
E358117
|
entity |
| Predicate | isStopNumber |
P113873
|
FINISHED |
| Object | 6 on the Shikoku 88-temple pilgrimage route |
—
|
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: 6 on the Shikoku 88-temple pilgrimage route | Statement: [Temple 6: Anrakuji, isStopNumber, 6 on the Shikoku 88-temple pilgrimage route]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isStopNumber Context triple: [Temple 6: Anrakuji, isStopNumber, 6 on the Shikoku 88-temple pilgrimage route]
-
A.
stationNumber
Indicates the specific station identifier or code assigned to an entity within a system or network.
-
B.
hasStopNear
Indicates that one entity has a stop or stopping point located in close proximity to another entity.
-
C.
hasStopType
Indicates that a stop or stopping point is classified as having a particular type or category of stop.
-
D.
hasStopArea
Indicates that an entity is associated with or contains a specific stop area, such as a designated location where vehicles stop.
-
E.
pilgrimageStopNumber
chosen
Indicates the ordinal position of a specific stop or location within a defined pilgrimage route or sequence.
- 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_69d8279402a88190821ffa39ae15bccf |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de915d28ec81909e72124e9dd67bfb |
completed | April 14, 2026, 7:11 p.m. |
| PD | Predicate disambiguation | batch_69de5c3a02fc819097373f97a260cdeb |
completed | April 14, 2026, 3:24 p.m. |
Created at: April 10, 2026, 1:18 a.m.