Triple
T26501494
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mrs. Risley |
E669435
|
entity |
| Predicate | livesInFictionalWork |
P47688
|
FINISHED |
| Object | Toronto |
—
|
NE NERFINISHED |
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: Toronto | Statement: [Mrs. Risley, livesInFictionalWork, Toronto]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: livesInFictionalWork Context triple: [Mrs. Risley, livesInFictionalWork, Toronto]
-
A.
livesInFiction
Indicates that one entity exists or resides within the fictional world or narrative setting created by another entity.
-
B.
residesInFictionalLocation
chosen
Indicates that an entity lives or is based in a location that is explicitly fictional or imaginary.
-
C.
worksInFictionalContext
Indicates that an entity performs work or fulfills a role within a fictional or imagined setting rather than in real-world circumstances.
-
D.
livesNearFictionalPlace
Indicates that one entity resides in close proximity to a fictional or imaginary location.
-
E.
locatedInFictionalContext
Indicates that one entity exists or occurs within the setting or universe of a fictional work associated with another entity.
- 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_69eeb319ec70819090834c2591cf5f1e |
completed | April 27, 2026, 12:51 a.m. |
| NER | Named-entity recognition | batch_69f6135a809c81909e74dad63931f08a |
completed | May 2, 2026, 3:08 p.m. |
| PD | Predicate disambiguation | batch_69f602d5c8808190a1fdbebd6f0981e8 |
completed | May 2, 2026, 1:57 p.m. |
Created at: April 27, 2026, 1:13 a.m.