Triple
T6433679
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tokyo Metro 10000 series |
E129838
|
entity |
| Predicate | hasLongitudinalSeating |
P70594
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Tokyo Metro 10000 series, hasLongitudinalSeating, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLongitudinalSeating Context triple: [Tokyo Metro 10000 series, hasLongitudinalSeating, yes]
-
A.
hasSeating
Indicates that one entity provides or contains seating capacity or seating arrangements for another entity.
-
B.
hasSeat
Indicates that one entity possesses, provides, or includes a seat for another entity.
-
C.
hasBermSeating
Indicates that a venue or location includes berm-style seating areas, typically grass-covered embankments where spectators can sit.
-
D.
isAllSeater
Indicates that the entity provides only seated accommodation, with no standing room available.
-
E.
hasSeatingPose
Indicates that an entity is in a seated posture or arrangement, specifying how it is positioned while sitting.
- 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_69c0084caac48190a7bc2ad8ba44536f |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0693f73ec8190883470b57f8141aa |
completed | March 22, 2026, 10:12 p.m. |
| PD | Predicate disambiguation | batch_69c060f96980819091bab9335922a457 |
completed | March 22, 2026, 9:36 p.m. |
| PDg | Predicate description generation | batch_69c0623e3cd48190929b0e3cba013909 |
completed | March 22, 2026, 9:42 p.m. |
Created at: March 22, 2026, 4:45 p.m.