Triple
T26590386
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tokyo Metropolitan Central Library |
E667335
|
entity |
| Predicate | buildingFloorsBelowGround |
P996
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Tokyo Metropolitan Central Library, buildingFloorsBelowGround, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: buildingFloorsBelowGround Context triple: [Tokyo Metropolitan Central Library, buildingFloorsBelowGround, 2]
-
A.
hasUndergroundLevel
Indicates that one entity possesses or includes a level, floor, or section that is located below ground level.
-
B.
undergroundZone
Indicates that something is located in, associated with, or occurring within an area beneath the ground surface.
-
C.
hasUndergroundDepth
Indicates that one entity has a specified vertical extent or depth located below the ground surface relative to another reference or context.
-
D.
numberOfBasementLevels
chosen
Indicates the total count of basement levels associated with a given structure or property.
-
E.
hasUndergroundFacilities
Indicates that one entity possesses or contains facilities or infrastructure located below ground level in relation to another entity.
- 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_69ee9cfc385081909ac9ae178030a06e |
completed | April 26, 2026, 11:17 p.m. |
| NER | Named-entity recognition | batch_69f62d53ad58819080c5227c7a729d15 |
completed | May 2, 2026, 4:58 p.m. |
| PD | Predicate disambiguation | batch_69f62c15952881908a5ea0c25904afec |
completed | May 2, 2026, 4:53 p.m. |
Created at: April 27, 2026, 2:07 a.m.