Triple
T21561121
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charles K. French |
E532029
|
entity |
| Predicate | appearedInNumberOfWorks |
P6221
|
FINISHED |
| Object | hundreds of films |
—
|
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: hundreds of films | Statement: [Charles K. French, appearedInNumberOfWorks, hundreds of films]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: appearedInNumberOfWorks Context triple: [Charles K. French, appearedInNumberOfWorks, hundreds of films]
-
A.
appearsAcrossNumberOfNovels
Indicates the number of distinct novels in which a given entity makes an appearance.
-
B.
authorOfWorkHeAppearsIn
Indicates that a person is the author of a work in which he himself appears as a character or subject.
-
C.
attestedInWorksOf
Indicates that something (such as a claim, form, or usage) is documented or evidenced within the works produced by a particular author or creator.
-
D.
themeOfWorkHeAppearsIn
Indicates that the subject is the thematic focus or central topic of the work in which he appears.
-
E.
numberOfWorks
chosen
Indicates the total count of works associated with a given 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_69e0c460db088190828c64206a450273 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69eed2e3e2348190b5c3b66cdc871e6e |
completed | April 27, 2026, 3:07 a.m. |
| PD | Predicate disambiguation | batch_69e6320c8c2c81908bf031447d66a052 |
completed | April 20, 2026, 2:02 p.m. |
Created at: April 16, 2026, 6:29 p.m.