Triple
T33819192
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sister Evangelina |
E866778
|
entity |
| Predicate | areaOfFictionalSetting |
P114636
|
FINISHED |
| Object | East End of London |
—
|
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: East End of London | Statement: [Sister Evangelina, areaOfFictionalSetting, East End of London]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: areaOfFictionalSetting Context triple: [Sister Evangelina, areaOfFictionalSetting, East End of London]
-
A.
fictionalAreaSquareMiles
Indicates the total land or surface area, measured in square miles, attributed to a fictional or imaginary place.
-
B.
fictionalSettingRegion
chosen
Indicates that a fictional setting is located within or associated with a specific geographic or administrative region.
-
C.
stateOfFictionalLocation
Indicates that a fictional location is situated within or belongs to a particular state or state-like administrative region.
-
D.
basedInFictionalSetting
Indicates that an entity’s primary location or setting exists within a fictional or imaginary world rather than the real world.
-
E.
hasFictionalSettingElement
Indicates that something includes or is associated with a specific element or component of a fictional setting.
- 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_69f349911a8c81908478662194b23d8c |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6fff809b881909c0c303f693eb3bc |
completed | May 3, 2026, 7:57 a.m. |
| PD | Predicate disambiguation | batch_69f6fc59518081908b0275f47721d561 |
completed | May 3, 2026, 7:42 a.m. |
Created at: May 1, 2026, 1:46 a.m.