Triple
T9435467
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Iwashimizu Hachimangū |
E227492
|
entity |
| Predicate | accessRailway |
P57688
|
FINISHED |
| Object | Keihan Main Line |
—
|
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: Keihan Main Line | Statement: [Iwashimizu Hachimangū, accessRailway, Keihan Main Line]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: accessRailway Context triple: [Iwashimizu Hachimangū, accessRailway, Keihan Main Line]
-
A.
railwayAccess
chosen
Indicates that an entity has direct access to, connection with, or service by a railway line or station.
-
B.
railAccessModel
Indicates the type or pattern of how rail infrastructure or services are accessed or connected between locations or entities.
-
C.
coordinatesRailTrafficWith
Indicates that one entity organizes and synchronizes rail operations or movements with another entity to ensure coordinated train traffic.
-
D.
railroadMet
Indicates that two or more railroads encountered or connected with each other at a specific place or time.
-
E.
connectsToRailStation
Indicates that one entity has a direct link, route, or access connection to a rail station.
- 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_69ca8437a7ac81908651de48f2d2141d |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd7e64109081908222f590928bc572 |
completed | April 1, 2026, 8:21 p.m. |
| PD | Predicate disambiguation | batch_69cca55548488190b171ae695a3212de |
completed | April 1, 2026, 4:55 a.m. |
Created at: March 30, 2026, 7:50 p.m.