Triple
T4257428
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maggie Hartford |
E96010
|
entity |
| Predicate | hasOccupationInFiction |
P34569
|
FINISHED |
| Object | vineyard-related role |
—
|
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: vineyard-related role | Statement: [Maggie Hartford, hasOccupationInFiction, vineyard-related role]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOccupationInFiction Context triple: [Maggie Hartford, hasOccupationInFiction, vineyard-related role]
-
A.
hasFictionalRole
Indicates that an entity plays or is assigned a specific role within a fictional work or narrative.
-
B.
fictionalOccupation
chosen
Indicates that one entity is the imaginary or narrative-based job, role, or profession attributed to another entity within a fictional context.
-
C.
employsFictionalCharacter
Indicates that one entity (typically an organization or individual) has hired or uses the services of a fictional character in some capacity.
-
D.
hasNotableFictionalBearer
Indicates that an entity is associated with at least one well-known fictional character that bears its name or designation.
-
E.
hasReputationInFiction
Indicates that an entity is known or regarded in a particular way within fictional works or narratives.
- 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_69b3454095ac81909c2494f7ff294af1 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b34ec321008190b2cc1aca6ab690c4 |
completed | March 12, 2026, 11:39 p.m. |
| PD | Predicate disambiguation | batch_69b347f73e008190a908a48ef389945a |
completed | March 12, 2026, 11:10 p.m. |
Created at: March 12, 2026, 11:06 p.m.