Triple
T36787075
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Donna’s taverna |
E908947
|
entity |
| Predicate | fictionalOwnerOccupation |
P14482
|
FINISHED |
| Object | single mother |
—
|
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: single mother | Statement: [Donna’s taverna, fictionalOwnerOccupation, single mother]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalOwnerOccupation Context triple: [Donna’s taverna, fictionalOwnerOccupation, single mother]
-
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.
hasFictionalProprietor
chosen
Indicates that something is owned, managed, or run by a fictional character or entity within a narrative context.
-
C.
fictionalProfessionSpecialty
Indicates that a fictional character’s professional role is specialized in a particular subfield, focus area, or niche within that profession.
-
D.
fictionalCareerStatus
Indicates the relationship between an entity and a career or professional role that exists only in a fictional or imagined context, rather than in real life.
-
E.
fictionalResidence
Indicates that one entity is the place where another entity lives or is based within a fictional or imaginary context.
- 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_69f76e7a937c81909ed7359641e670f6 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69ff409ff5548190849c2d50e99bd807 |
completed | May 9, 2026, 2:11 p.m. |
| PD | Predicate disambiguation | batch_69ff401a5e188190a72f945e910b4a6c |
completed | May 9, 2026, 2:09 p.m. |
Created at: May 3, 2026, 4:12 p.m.