Triple
T35136965
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | River City public library |
E1014598
|
entity |
| Predicate | fictionalOccupationAtLocation |
P125184
|
FINISHED |
| Object | librarian |
—
|
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: librarian | Statement: [River City public library, fictionalOccupationAtLocation, librarian]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalOccupationAtLocation Context triple: [River City public library, fictionalOccupationAtLocation, librarian]
-
A.
fictionalOccupation
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.
laterOccupationInFiction
Indicates that a fictional character holds a particular occupation at a later point in the narrative or timeline, distinct from their earlier roles.
-
D.
workLocationOfFictionalCharacter
Indicates the place or organization where a fictional character is depicted as working within their narrative context.
-
E.
worksAtFictionalPlace
chosen
Indicates that an entity is employed at or associated with performing work in a fictional or imaginary location.
- 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_69f76dd9c1848190af70d4882a2c1ad7 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69fee691952c8190822da83e46311d1d |
completed | May 9, 2026, 7:47 a.m. |
| PD | Predicate disambiguation | batch_69fee62f285c8190a625562a9b80526e |
completed | May 9, 2026, 7:45 a.m. |
Created at: May 3, 2026, 4:02 p.m.