Triple
T13763323
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 500 series Shinkansen |
E330669
|
entity |
| Predicate | laterService |
P13710
|
FINISHED |
| Object | Kodama |
E307376
|
NE 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: Kodama | Statement: [500 series Shinkansen, laterService, Kodama]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kodama Context triple: [500 series Shinkansen, laterService, Kodama]
-
A.
Kodama
Kodama is a Japanese surname borne by various notable figures in fields such as politics, the military, the arts, and sports.
-
B.
Kodama
chosen
Kodama is a Japanese Shinkansen train service known for its all-stop, slower-speed runs along high-speed rail lines such as the Tokaido Shinkansen.
-
C.
Warabi
Warabi is a small, densely populated city in Japan’s Saitama Prefecture, known for its convenient access to central Tokyo and residential character.
-
D.
Moruya
Moruya is a coastal town in New South Wales, Australia, known for its scenic river setting, nearby beaches, and historic granite quarries.
-
E.
Yokadouma
Yokadouma is a town in eastern Cameroon that serves as an important local administrative and commercial center near the country's forested border regions.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d81c583b0081909e408a17db517a21 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de022690ac8190bd5410ecc659a2a7 |
completed | April 14, 2026, 9 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7ce64676c8190921796666ecd3dba |
completed | May 3, 2026, 10:38 p.m. |
Created at: April 9, 2026, 10:10 p.m.