Triple
T38239079
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Agnes Tellwright |
E1013707
|
entity |
| Predicate | fictionalSettingTown |
P182071
|
FINISHED |
| Object | Bursley |
—
|
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: Bursley | Statement: [Agnes Tellwright, fictionalSettingTown, Bursley]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalSettingTown Context triple: [Agnes Tellwright, fictionalSettingTown, Bursley]
-
A.
fictionalTownName
Indicates that the entity is associated with the name of a town that exists only in fiction rather than in the real world.
-
B.
townOfFictionalSetting
chosen
Indicates that a town serves as the fictional setting or primary location where the events of a narrative work take place.
-
C.
fictionalTownFeatured
Indicates that a fictional town is prominently depicted or serves as a key setting within a work or medium.
-
D.
hasFictionalNearbyTown
Indicates that an entity is associated with a fictional town located in its vicinity or surrounding area.
-
E.
fictionalCitySetting
Indicates that a narrative, event, or work is set in a city that is imaginary or does not exist in the real world.
- 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_69f76dd72a248190a5fe18db2bd1eb15 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69ff76ac40988190a34d858b5472ee2b |
completed | May 9, 2026, 6:02 p.m. |
| PD | Predicate disambiguation | batch_69ff760a90948190a12fcb80e6e3e14b |
completed | May 9, 2026, 5:59 p.m. |
Created at: May 3, 2026, 4:30 p.m.