Triple
T13763321
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 500 series Shinkansen |
E330669
|
entity |
| Predicate | primaryService |
P11934
|
FINISHED |
| Object | Nozomi |
E306032
|
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: Nozomi | Statement: [500 series Shinkansen, primaryService, Nozomi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nozomi Context triple: [500 series Shinkansen, primaryService, Nozomi]
-
A.
Nozomi
chosen
Nozomi is the fastest and most premium Shinkansen (bullet train) service operating on Japan’s Tokaido and Sanyo lines, known for its high speed and frequent departures between major cities like Tokyo and Osaka.
-
B.
Takako
Takako is a Japanese feminine given name borne by various notable figures in politics, arts, and entertainment.
-
C.
Sanae
Sanae is a Japanese feminine given name borne by various notable figures in politics, entertainment, and other fields.
-
D.
Naoko
Naoko is a central, emotionally fragile character in Haruki Murakami’s story "Norwegian Wood," whose complex relationship with the protagonist explores themes of love, loss, and mental illness.
-
E.
Mayumi
Mayumi is a Japanese surname borne by various individuals, including Akinobu Mayumi, and can also be used as a given name.
- 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_69f7b8d28cc88190af46b86473af0a1c |
completed | May 3, 2026, 9:06 p.m. |
Created at: April 9, 2026, 10:10 p.m.