Triple
T4895702
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Booker |
E109671
|
entity |
| Predicate | authorNationalityOfWork |
P6689
|
FINISHED |
| Object | American |
—
|
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: American | Statement: [Booker, authorNationalityOfWork, American]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: authorNationalityOfWork Context triple: [Booker, authorNationalityOfWork, American]
-
A.
authorNationality
chosen
Indicates the relationship between an author and the country or nationality with which that author is identified.
-
B.
creatorNationality
Indicates that the creator of an entity has a specified national affiliation or citizenship.
-
C.
associatedComposerNationality
Indicates that there is a relationship between a composer and a specific nationality with which that composer is identified or associated.
-
D.
primaryArtistNationality
Indicates the nationality associated with the main or primary artist involved in a work or context.
-
E.
architectNationality
Indicates the country or national affiliation associated with an architect.
- 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_69bd4410bbf88190aad50d2451c863d6 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6ffabccc81909115ece1b04e2061 |
completed | March 20, 2026, 4:04 p.m. |
| PD | Predicate disambiguation | batch_69bd6c2e7b5c8190b8bf9d616dfa24f0 |
completed | March 20, 2026, 3:47 p.m. |
Created at: March 20, 2026, 1:28 p.m.