Triple
T15161304
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tobu Department Store Ikebukuro |
E362222
|
entity |
| Predicate | hasRestaurantFloors |
P116970
|
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: [Tobu Department Store Ikebukuro, hasRestaurantFloors, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRestaurantFloors Context triple: [Tobu Department Store Ikebukuro, hasRestaurantFloors, yes]
-
A.
hasFloorsAboveGround
Indicates that an entity (typically a building or structure) possesses a specified number of floors that are located above ground level.
-
B.
hasOfficeFloors
Indicates that one entity (typically a building or structure) contains floors that are designated or used as office space.
-
C.
numberOfFloorsServed
Indicates the total count of distinct floors that are served or accessed by a given entity (such as an elevator or service system).
-
D.
numberOfFloors
Indicates the total count of distinct floor levels that a building or structure has.
-
E.
locatedInBuildingFloorCount
Indicates that one entity is located in or associated with a building characterized by a specific number of floors.
- 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_69d85a087b7c81908baa94a53dac8d68 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0060f2efc8190aa0eb5fb8d4ce085 |
completed | April 15, 2026, 9:41 p.m. |
| PD | Predicate disambiguation | batch_69deb9779acc81908ed2dad382c42dca |
completed | April 14, 2026, 10:02 p.m. |
| PDg | Predicate description generation | batch_69dec72059c08190a34f513a00185b08 |
completed | April 14, 2026, 11 p.m. |
Created at: April 10, 2026, 3:08 a.m.