Triple
T33655151
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Geneva Pine |
E862200
|
entity |
| Predicate | basedInFictionalProfession |
P34569
|
FINISHED |
| Object | law |
—
|
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: law | Statement: [Geneva Pine, basedInFictionalProfession, law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: basedInFictionalProfession Context triple: [Geneva Pine, basedInFictionalProfession, law]
-
A.
fictionalOccupation
chosen
Indicates that one entity is the imaginary or narrative-based job, role, or profession attributed to another entity within a fictional context.
-
B.
fictionalProfessionSpecialty
Indicates that a fictional character’s professional role is specialized in a particular subfield, focus area, or niche within that profession.
-
C.
hasFictionalProfessionLevel
Indicates that an entity holds a fictional or imagined profession at a specified level, rank, or degree of expertise.
-
D.
laterOccupationInFiction
Indicates that a fictional character holds a particular occupation at a later point in the narrative or timeline, distinct from their earlier roles.
-
E.
portrayedProfessionOfCharacter
Indicates that one entity is the profession or occupation depicted as being held by a particular character.
- 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_69f349840ba881908e3bfce536aeb92b |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69fecd0a732c819097bdd3eb69b6158c |
completed | May 9, 2026, 5:58 a.m. |
| PD | Predicate disambiguation | batch_69fecc0318d481908b5b20598a76a9fe |
completed | May 9, 2026, 5:54 a.m. |
Created at: May 1, 2026, 1:42 a.m.