Triple
T35315878
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tulliver |
E1019903
|
entity |
| Predicate | hasFictionalOccupationAssociation |
P34569
|
FINISHED |
| Object | milling |
—
|
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: milling | Statement: [Tulliver, hasFictionalOccupationAssociation, milling]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalOccupationAssociation Context triple: [Tulliver, hasFictionalOccupationAssociation, milling]
-
A.
hasFictionalProfessionLevel
Indicates that an entity holds a fictional or imagined profession at a specified level, rank, or degree of expertise.
-
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.
hasFictionalRole
Indicates that an entity plays or is assigned a specific role within a fictional work or narrative.
-
D.
hasOccupationInReality
Indicates that an entity holds or performs a specific occupation in the real world, as opposed to fictional or hypothetical contexts.
-
E.
fictionalProfessionStatus
Indicates that an entity holds, has held, or is described as holding a profession or occupational role that is fictional rather than real.
- 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_69f76de9d45c81908a2ed0956b448b65 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69ffc7b4c7f88190b6357a44e7f0940f |
completed | May 9, 2026, 11:48 p.m. |
| PD | Predicate disambiguation | batch_69ffc755f09c8190995ca00d97336988 |
completed | May 9, 2026, 11:46 p.m. |
Created at: May 3, 2026, 4:03 p.m.