Triple
T16390533
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 800 series Shinkansen |
E398038
|
entity |
| Predicate | usedOnService |
P2367
|
FINISHED |
| Object | Sakura |
E328289
|
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: Sakura | Statement: [800 series Shinkansen, usedOnService, Sakura]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sakura Context triple: [800 series Shinkansen, usedOnService, Sakura]
-
A.
Sakura
chosen
Sakura is a Japanese high-speed Shinkansen train service that operates mainly on the Sanyo and Kyushu Shinkansen lines.
-
B.
Sōsa
Sōsa is a coastal city in Chiba Prefecture, Japan, known for its proximity to the long sandy stretch of Kujūkuri Beach along the Pacific Ocean.
-
C.
Hana
Hana is a compassionate Canadian army nurse in Michael Ondaatje's novel "The English Patient," who cares for a badly burned man in an abandoned Italian villa during World War II.
-
D.
Hana
Hana is a common female given name of Hebrew origin, often associated with meanings like "grace" or "favor."
-
E.
Hana
Hana is a small, remote town on the eastern coast of Maui, Hawaii, known for its lush landscapes, waterfalls, and the scenic Road to Hana.
- 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_69d87f2880b48190ae1a9673a3bbef80 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e326425c8081908cacffcfa8c7386b |
completed | April 18, 2026, 6:35 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00357167b881909a5182537ef973ce |
completed | May 10, 2026, 7:36 a.m. |
Created at: April 10, 2026, 5:08 a.m.