Triple
T5936371
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | He Art Museum |
E132054
|
entity |
| Predicate | designedByNationality |
P35634
|
FINISHED |
| Object | Japanese architect |
—
|
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: Japanese architect | Statement: [He Art Museum, designedByNationality, Japanese architect]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: designedByNationality Context triple: [He Art Museum, designedByNationality, Japanese architect]
-
A.
designerNationality
chosen
Indicates that a designer has a specific national or country affiliation.
-
B.
designedBy
Indicates that one entity is the creator, planner, or architect responsible for the form or structure of another entity.
-
C.
designedIn
Indicates that something was created, planned, or conceived during a particular time period or at a specific location.
-
D.
architectNationality
Indicates the country or national affiliation associated with an architect.
-
E.
designedByCompanyFoundedBy
Indicates that something was designed by a company that was founded by a specified 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_69c0085c55dc8190aa90e242c956e2fa |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03f26f51881908cc253fe5775a1fc |
completed | March 22, 2026, 7:12 p.m. |
| PD | Predicate disambiguation | batch_69c03355caf08190b960563a1aed23f9 |
completed | March 22, 2026, 6:22 p.m. |
Created at: March 22, 2026, 4:01 p.m.