Triple
T8319579
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thumbprint |
E194794
|
entity |
| Predicate | featuresCharacterWithBackground |
P23263
|
FINISHED |
| Object | U.S. military prison guard |
—
|
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: U.S. military prison guard | Statement: [Thumbprint, featuresCharacterWithBackground, U.S. military prison guard]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresCharacterWithBackground Context triple: [Thumbprint, featuresCharacterWithBackground, U.S. military prison guard]
-
A.
featuresCharactersFrom
Indicates that one entity (such as a work or production) includes or presents characters originating from another entity.
-
B.
featuresCharacterRole
chosen
Indicates that a work includes a character appearing in a specific narrative or functional role.
-
C.
hasProtagonistBackground
Indicates that a work or narrative features a specified background or origin story for its main protagonist.
-
D.
characterTheme
Indicates that a particular theme, motif, or conceptual focus is associated with a given character.
-
E.
character2
Indicates that a second character entity is involved in the relationship or context defined by the predicate.
- 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_69ca82e7a8a88190a32bb5cc0feb012d |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7f6686a0819094abc2bfd2e500a5 |
completed | March 31, 2026, 8:01 a.m. |
| PD | Predicate disambiguation | batch_69cb70bf689c8190a9d9b6b872abf53d |
completed | March 31, 2026, 6:59 a.m. |
Created at: March 30, 2026, 5:55 p.m.