Triple
T18402277
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rawene |
E450024
|
entity |
| Predicate | distanceToKerikeri |
P130990
|
FINISHED |
| Object | approximately 85 kilometres southwest |
—
|
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 85 kilometres southwest | Statement: [Rawene, distanceToKerikeri, approximately 85 kilometres southwest]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToKerikeri Context triple: [Rawene, distanceToKerikeri, approximately 85 kilometres southwest]
-
A.
distanceFromKochi_km
Indicates the physical distance, measured in kilometers, between a given location and Kochi.
-
B.
distanceToKyushu
Indicates the spatial distance between a given entity’s location and the region of Kyushu.
-
C.
distanceFromNaha
Indicates the spatial distance between a given location and Naha.
-
D.
distanceToSapporo
Indicates the measured or calculated distance between a given entity and the location of Sapporo.
-
E.
distanceToŌitaCityByRoad
Indicates the road travel distance between a given place and Ōita City.
- 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_69d8b9fab8a8819086a9ddc0871715e0 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e519537eb88190bc85d21471e27c26 |
completed | April 19, 2026, 6:05 p.m. |
| PD | Predicate disambiguation | batch_69e44ff1f92c8190afbb8e85d12bf2a9 |
completed | April 19, 2026, 3:45 a.m. |
| PDg | Predicate description generation | batch_69e451a1bda48190a9cd1db436d4be62 |
completed | April 19, 2026, 3:53 a.m. |
Created at: April 10, 2026, 10:46 a.m.