Triple
T12722630
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uşak |
E304022
|
entity |
| Predicate | distanceToİzmir |
P106571
|
FINISHED |
| Object | approximately 210 kilometres |
—
|
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: approximately 210 kilometres | Statement: [Uşak, distanceToİzmir, approximately 210 kilometres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToİzmir Context triple: [Uşak, distanceToİzmir, approximately 210 kilometres]
-
A.
distanceToIstanbulApproxKm
Indicates the approximate distance, measured in kilometers, between a given place and Istanbul.
-
B.
distanceFromKayseri
Indicates the spatial distance between a given entity and the location of Kayseri.
-
C.
approxDistanceToMytilene
Indicates that one entity is located at an approximate distance from the place or reference point named Mytilene.
-
D.
distanceToIbiza
Indicates the spatial distance between a given entity’s location and the location of Ibiza.
-
E.
distanceToThessaloniki
Indicates the spatial distance between a given entity’s location and the city of Thessaloniki.
- F. None of above. chosen
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_69d7bdf084148190ab9d513dc0735af4 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96d89ea70819098c470344f172167 |
completed | April 10, 2026, 9:37 p.m. |
| PD | Predicate disambiguation | batch_69d96403957c81909acdee7bdae71696 |
completed | April 10, 2026, 8:56 p.m. |
| PDg | Predicate description generation | batch_69d96d87078c819083ea724238992204 |
completed | April 10, 2026, 9:37 p.m. |
Created at: April 9, 2026, 5:24 p.m.