Triple
T23555485
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Color Force |
E578176
|
entity |
| Predicate | basedOnWorkTypeFrequentlyAdapted |
P53538
|
FINISHED |
| Object | novels |
—
|
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: novels | Statement: [Color Force, basedOnWorkTypeFrequentlyAdapted, novels]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: basedOnWorkTypeFrequentlyAdapted Context triple: [Color Force, basedOnWorkTypeFrequentlyAdapted, novels]
-
A.
basedOnWorkAdaptedTo
chosen
Indicates that one work is derived from and adapted based on the content, story, or elements of another pre-existing work.
-
B.
hasWorkAdaptedBy
Indicates that one work has been transformed or re-created into another work, such as through adaptation into a different medium or format.
-
C.
hasWorkAdaptationType
Indicates the type of adaptation or modification applied to a work (e.g., translation, abridgment, dramatization) in relation to its original form.
-
D.
workAdaptedFor
Indicates that one work has been modified or transformed to create another work specifically suited for a different medium, context, or audience.
-
E.
hasWorkAdaptation
Indicates that an entity has a modified, adjusted, or alternative form specifically adapted for use in a work or professional context.
- 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_69e245fa93448190919cb04534560542 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f1aed253788190ba75109af0e91b37 |
completed | April 29, 2026, 7:10 a.m. |
| PD | Predicate disambiguation | batch_69f118afabd88190bd88f49597d120e8 |
completed | April 28, 2026, 8:29 p.m. |
Created at: April 17, 2026, 6:12 p.m.