Triple
T20260606
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sendai Station |
E498821
|
entity |
| Predicate | travelTimeToTokyoByShinkansen_minutes |
P110673
|
FINISHED |
| Object | approximately 90 to 120 |
—
|
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 90 to 120 | Statement: [Sendai Station, travelTimeToTokyoByShinkansen_minutes, approximately 90 to 120]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: travelTimeToTokyoByShinkansen_minutes Context triple: [Sendai Station, travelTimeToTokyoByShinkansen_minutes, approximately 90 to 120]
-
A.
travelTimeFromTokyoByShinkansen
chosen
Indicates the amount of time required to travel from Tokyo to another location using the Shinkansen (bullet train).
-
B.
distanceFromShinOsakaByShinkansen
Indicates the travel distance between a given location and Shin-Osaka Station when using the Shinkansen (bullet train).
-
C.
distanceToSapporo
Indicates the measured or calculated distance between a given entity and the location of Sapporo.
-
D.
distanceFromTokyo
Indicates the physical distance between a given location and Tokyo.
-
E.
distanceToShinjukuStation_km
Indicates the physical distance, measured in kilometers, between a given place and Shinjuku Station.
- 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_69da6275fa6c8190952924930adee150 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e674ca77c081909cd2f44ccfe3662d |
completed | April 20, 2026, 6:47 p.m. |
| PD | Predicate disambiguation | batch_69e55b1b23f88190bdcbe2f81dd226dd |
completed | April 19, 2026, 10:45 p.m. |
Created at: April 11, 2026, 11:41 p.m.