Triple
T36002636
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Meitetsu Department Store |
E1041173
|
entity |
| Predicate | hasNearbyRailwayOperator |
P202026
|
FINISHED |
| Object | Nagoya Railroad |
—
|
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: Nagoya Railroad | Statement: [Meitetsu Department Store, hasNearbyRailwayOperator, Nagoya Railroad]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbyRailwayOperator Context triple: [Meitetsu Department Store, hasNearbyRailwayOperator, Nagoya Railroad]
-
A.
hasRailTransportOperator
Indicates that a rail transport system, service, or infrastructure is operated or managed by a specified rail transport operator.
-
B.
hasLocalRailOperator
Indicates that a specified rail operator is responsible for providing local or regional rail services within a particular area or network.
-
C.
hasRailOperatorNamedAfter
Indicates that a rail operator is named after the specified entity.
-
D.
hasNearbyRailway
Indicates that one entity is located close to a railway associated with or relevant to another entity.
-
E.
hasCommuterOperator
Indicates that an entity (such as a route, service, or station) is operated or served by a specific commuter transport operator.
- F. None of above. chosen
Provenance (4 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_69f76e2a02208190aedd1f9025a8b300 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a00474f08908190bc8ae3b320ec887b |
completed | May 10, 2026, 8:52 a.m. |
| PD | Predicate disambiguation | batch_6a0045dc3bd48190a9e0520f3ef3f067 |
completed | May 10, 2026, 8:46 a.m. |
| PDg | Predicate description generation | batch_6a00474e552c8190b711363081d29292 |
completed | May 10, 2026, 8:52 a.m. |
Created at: May 3, 2026, 4:07 p.m.