Triple
T13874502
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wakayama City |
E333543
|
entity |
| Predicate | distanceToOsakaApproxKm |
P42798
|
FINISHED |
| Object | about 60 |
—
|
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: about 60 | Statement: [Wakayama City, distanceToOsakaApproxKm, about 60]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToOsakaApproxKm Context triple: [Wakayama City, distanceToOsakaApproxKm, about 60]
-
A.
distanceFromOsaka
chosen
Indicates the measured distance between a given place or object and the city of Osaka.
-
B.
distanceFromShinOsakaByShinkansen
Indicates the travel distance between a given location and Shin-Osaka Station when using the Shinkansen (bullet train).
-
C.
distanceFromKochi_km
Indicates the physical distance, measured in kilometers, between a given location and Kochi.
-
D.
distanceFromKyoto
Indicates the measured spatial distance between a given entity’s location and the city of Kyoto.
-
E.
distanceToSapporo
Indicates the measured or calculated distance between a given entity and the location of Sapporo.
- F. None of above.
Provenance (3 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_69d81c5ced9c8190b0e9bcc6effe5959 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de23a101488190bd790b28033d38b9 |
completed | April 14, 2026, 11:23 a.m. |
| PD | Predicate disambiguation | batch_69de05972f3881909977b4c843984f88 |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 9, 2026, 10:14 p.m.