Triple
T33124708
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eveline |
E847689
|
entity |
| Predicate | setInCityContext |
P7747
|
FINISHED |
| Object | working-class Dublin |
—
|
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: working-class Dublin | Statement: [Eveline, setInCityContext, working-class Dublin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: setInCityContext Context triple: [Eveline, setInCityContext, working-class Dublin]
-
A.
setInCityModelledOn
Indicates that a fictional or constructed city is based on, inspired by, or patterned after a real-world city.
-
B.
isInCity
Indicates that one entity is located within the geographical boundaries of a specified city.
-
C.
isInUrbanContext
Indicates that something exists, occurs, or is situated within an urban or city-based environment or setting.
-
D.
settingCity
chosen
Indicates that a work or event takes place in, or is primarily located within, a particular city.
-
E.
formerCityContext
Indicates that the subject entity was formerly classified or recognized as a city within the specified contextual scope or time frame.
- 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_69f349588f088190b7c9588860f72033 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6db6af1d88190989810182354d60f |
completed | May 3, 2026, 5:21 a.m. |
| PD | Predicate disambiguation | batch_69f6d82d068c8190940a3200ed760e38 |
completed | May 3, 2026, 5:07 a.m. |
Created at: May 1, 2026, 1:27 a.m.