Triple
T23919132
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | To Sua Ocean Trench |
E602166
|
entity |
| Predicate | distanceFromApia |
P154363
|
FINISHED |
| Object | approximately 1.5 hours by car |
—
|
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 1.5 hours by car | Statement: [To Sua Ocean Trench, distanceFromApia, approximately 1.5 hours by car]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromApia Context triple: [To Sua Ocean Trench, distanceFromApia, approximately 1.5 hours by car]
-
A.
distanceToSeoul
Indicates the measured or estimated spatial distance between a given entity’s location and the city of Seoul.
-
B.
distanceToKoreanPeninsula
Indicates the measured or estimated spatial distance between a given entity or location and the Korean Peninsula.
-
C.
distanceFromGwangju
Indicates the spatial distance between a given location and the city of Gwangju.
-
D.
distanceFromKochi_km
Indicates the physical distance, measured in kilometers, between a given location and Kochi.
-
E.
distanceFromNaha
Indicates the spatial distance between a given location and Naha.
- 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_69e2953a187081908346a9f36e85fc98 |
completed | April 17, 2026, 8:16 p.m. |
| NER | Named-entity recognition | batch_69f1cf1600248190b38cfc93afb22a73 |
completed | April 29, 2026, 9:27 a.m. |
| PD | Predicate disambiguation | batch_69f16151ebdc819086e9e1d7cc1f4f3c |
completed | April 29, 2026, 1:39 a.m. |
| PDg | Predicate description generation | batch_69f16e348b548190b76e50f9b611f76d |
completed | April 29, 2026, 2:34 a.m. |
Created at: April 17, 2026, 8:41 p.m.