Triple
T25006367
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wall |
E625849
|
entity |
| Predicate | fictionalCountryContext |
P64113
|
FINISHED |
| Object | United Kingdom |
—
|
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: United Kingdom | Statement: [Wall, fictionalCountryContext, United Kingdom]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalCountryContext Context triple: [Wall, fictionalCountryContext, United Kingdom]
-
A.
fictionalCountryLocation
chosen
Indicates that a fictional country is located within, or geographically associated with, a specified place or region.
-
B.
countryOfFictionalContext
Indicates that a work of fiction is primarily set in, or contextually associated with, a particular country.
-
C.
fictionalCityContext
Indicates that the relationship or information is situated within, or pertains specifically to, the setting of a fictional city.
-
D.
fictionalPlaceType
Indicates that a place is a fictional location and specifies what type or category of fictional place it is.
-
E.
fictionalGeographicRegion
Indicates that a geographic region exists only in fiction or imagination rather than 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_69e2ff26c50481908bc82e799c9e6587 |
completed | April 18, 2026, 3:48 a.m. |
| NER | Named-entity recognition | batch_69f606c79ad081908369605f72e65ca6 |
completed | May 2, 2026, 2:14 p.m. |
| PD | Predicate disambiguation | batch_69f602ce79ec8190b8336c2b9de18ac7 |
completed | May 2, 2026, 1:57 p.m. |
Created at: April 18, 2026, 6:05 a.m.