Triple
T30657902
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Donald Blye |
E780437
|
entity |
| Predicate | playsRoleInBackstoryOf |
P119461
|
FINISHED |
| Object | Kensi Blye |
—
|
NE NERFINISHED |
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: Kensi Blye | Statement: [Donald Blye, playsRoleInBackstoryOf, Kensi Blye]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: playsRoleInBackstoryOf Context triple: [Donald Blye, playsRoleInBackstoryOf, Kensi Blye]
-
A.
roleInCharacterBackstory
chosen
Indicates that one entity plays a specific role or part in shaping another entity’s character backstory or personal history.
-
B.
playsInRole
Indicates that an entity performs or appears in a specific role within a production, event, or context.
-
C.
hasFictionalBackstory
Indicates that an entity is associated with an invented or imaginary narrative background rather than a real-world history.
-
D.
roleInStories
Indicates the specific function, position, or character part an entity plays within one or more stories.
-
E.
playedEarlyRoleIn
Indicates that one entity contributed significantly to the initial or formative stages of another entity’s development, success, or emergence.
- 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_69f224a6d10481909290be1a00fc83b3 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f6db1f3ec48190a82e7d893d3c76ba |
completed | May 3, 2026, 5:20 a.m. |
| PD | Predicate disambiguation | batch_69f6d82adfa481908a5e196d2e18c73f |
completed | May 3, 2026, 5:07 a.m. |
Created at: April 29, 2026, 8:30 p.m.