Triple
T34219645
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oral |
E877888
|
entity |
| Predicate | locatedNearGeographicalBoundary |
P108932
|
FINISHED |
| Object | Europe–Asia boundary |
E476858
|
NE 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: Europe–Asia boundary | Statement: [Oral, locatedNearGeographicalBoundary, Europe–Asia boundary]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: locatedNearGeographicalBoundary Context triple: [Oral, locatedNearGeographicalBoundary, Europe–Asia boundary]
-
A.
nearInternationalBoundary
Indicates that one entity is located close to an international boundary separating two or more countries.
-
B.
locatedNearStateBorderWith
Indicates that one entity is situated geographically close to the border of a specified state.
-
C.
geographicBoundary
chosen
Indicates that one entity serves as a limiting border or edge that defines the geographic extent or separation of another entity.
-
D.
locatedNearRegion
Indicates that one entity is situated in close geographic proximity to a specified region.
-
E.
locatedNearAdministrativeEntity
Indicates that one entity is geographically situated close to, or in the immediate vicinity of, an administrative unit such as a city, district, or region.
- F. None of above.
Provenance (4 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_69f349b0b4bc819088c1552424089ee9 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_6a037c8ae0248190b7e2ce4bf852c22d |
completed | May 12, 2026, 7:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a36d5df1bf081908a08af474ebc0c5e |
completed | June 20, 2026, 6:03 p.m. |
| PD | Predicate disambiguation | batch_6a0379f963908190846d232f386fd98f |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 1:55 a.m.