Triple
T1923685
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anisfield-Wolf Book Award |
E40179
|
entity |
| Predicate | languageOfWorksAwarded |
P17914
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [Anisfield-Wolf Book Award, languageOfWorksAwarded, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfWorksAwarded Context triple: [Anisfield-Wolf Book Award, languageOfWorksAwarded, English]
-
A.
awardNameLanguage
Indicates the language in which the name of an award is expressed.
-
B.
languageOfWritings
chosen
Indicates that a specified language is the one in which certain writings or written works are composed.
-
C.
notableAwardWork
Indicates that a work is the specific creation (e.g., book, film, artwork) for which an award or honor was given.
-
D.
authorAwardedForBodyOfWork
Indicates that an author received an award recognizing their entire body of work rather than a single specific piece.
-
E.
awardReceivedByWork
Indicates that a particular award was given in recognition of a specific work (such as a book, film, or artwork).
- 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_69a8864298748190a2f2fd34f7ef8d77 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb2359ca0819082b514a34c469b21 |
completed | March 7, 2026, 5:05 a.m. |
| PD | Predicate disambiguation | batch_69abafed2ab481908920334e77b1021b |
completed | March 7, 2026, 4:56 a.m. |
Created at: March 4, 2026, 7:35 p.m.