Triple
T13316588
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kintetsu Department Store |
E317202
|
entity |
| Predicate | buildingTypeOfFlagship |
P1844
|
FINISHED |
| Object | skyscraper |
—
|
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: skyscraper | Statement: [Kintetsu Department Store, buildingTypeOfFlagship, skyscraper]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: buildingTypeOfFlagship Context triple: [Kintetsu Department Store, buildingTypeOfFlagship, skyscraper]
-
A.
buildingType
chosen
Indicates the specific category or function that characterizes what kind of building something is.
-
B.
campusType
Indicates the classification or category of a campus based on its type (e.g., main, satellite, urban, rural).
-
C.
campusOfUniversityType
Indicates that a campus belongs to, is part of, or is categorized under a particular type of university.
-
D.
hasFlagshipCampusOf
Indicates that one location serves as the primary or main campus for a particular institution or organization.
-
E.
typeOfEnterpriseBuilt
Indicates the specific kind or category of enterprise that has been constructed or established.
- 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_69d806b4d62c81908d4ced1665414be5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99cfdc9388190af1fdd3cd4717bd8 |
completed | April 11, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69d98f6babd88190a5d529df9584b9a4 |
completed | April 11, 2026, 12:01 a.m. |
Created at: April 9, 2026, 9:29 p.m.