Triple
T14250770
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ikenoue Station |
E353253
|
entity |
| Predicate | distanceFromShibuyaOnKeioInokashiraLine |
P113400
|
FINISHED |
| Object | approximately 3.0 km |
—
|
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 3.0 km | Statement: [Ikenoue Station, distanceFromShibuyaOnKeioInokashiraLine, approximately 3.0 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromShibuyaOnKeioInokashiraLine Context triple: [Ikenoue Station, distanceFromShibuyaOnKeioInokashiraLine, approximately 3.0 km]
-
A.
distanceToShinjukuStation_km
Indicates the physical distance, measured in kilometers, between a given place and Shinjuku Station.
-
B.
distanceFromTokyoStationOnYokosukaLine
Indicates the distance of a location from Tokyo Station measured specifically along the Yokosuka Line route.
-
C.
distanceFromShinOsakaByShinkansen
Indicates the travel distance between a given location and Shin-Osaka Station when using the Shinkansen (bullet train).
-
D.
distanceFromKyotoStation
Indicates the spatial distance between a given location and Kyoto Station.
-
E.
distanceToIōtō
Indicates the spatial distance between a subject and the location Iōtō.
- 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_69d8278c43e08190824146f4632b89a5 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de6296f9d0819086f62f525d07eb12 |
completed | April 14, 2026, 3:51 p.m. |
| PD | Predicate disambiguation | batch_69de05c09b7881908acbca18bd7d997c |
completed | April 14, 2026, 9:15 a.m. |
| PDg | Predicate description generation | batch_69de239bd0f48190ada38c0261e0ef3c |
completed | April 14, 2026, 11:23 a.m. |
Created at: April 10, 2026, 1:08 a.m.