Triple
T13480417
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | King William's Town |
E318356
|
entity |
| Predicate | distanceToBhisho |
P110554
|
FINISHED |
| Object | approximately 10 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 10 kilometres | Statement: [King William's Town, distanceToBhisho, approximately 10 kilometres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToBhisho Context triple: [King William's Town, distanceToBhisho, approximately 10 kilometres]
-
A.
distanceToBingham
Indicates the measured or calculated distance between a given entity and the location named Bingham.
-
B.
distanceToBawtry
Indicates the spatial distance between a given entity and the location of Bawtry.
-
C.
approxDistanceToJos
Indicates that one entity is located at an approximate distance from the entity referred to as Jos.
-
D.
distanceToMoshi
Indicates the spatial distance between a given entity and the location Moshi.
-
E.
distanceToPahiatua
Indicates the measured spatial distance between a given location and the place referred to as Pahiatua.
- 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_69d806b6bfec819089222715b2e86c8e |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbaf36c6b08190ba99400600e0b662 |
completed | April 12, 2026, 2:41 p.m. |
| PD | Predicate disambiguation | batch_69dbadfddefc81909ef7fde23b181b5c |
completed | April 12, 2026, 2:36 p.m. |
| PDg | Predicate description generation | batch_69dbaecc98cc8190829f5be759c4f1e3 |
completed | April 12, 2026, 2:40 p.m. |
Created at: April 9, 2026, 9:42 p.m.