Triple

T9916951
Position Surface form Disambiguated ID Type / Status
Subject Tōhoku Shinkansen E185892 entity
Predicate serviceType P87 FINISHED
Object Yamabiko E626154 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: Yamabiko | Statement: [Tōhoku Shinkansen, serviceType, Yamabiko]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Yamabiko
Context triple: [Tōhoku Shinkansen, serviceType, Yamabiko]
  • A. Yamabiko chosen
    Yamabiko is a high-speed Shinkansen train service in Japan that operates on the Tōhoku Shinkansen line, connecting Tokyo with northern regions such as Sendai.
  • B. Shimotsuki
    Shimotsuki was a Japanese destroyer of the Imperial Japanese Navy that served in World War II before being sunk in late 1944.
  • C. Yamakoshi
    Yamakoshi is a recurring character from the Disney XD sitcom "Pair of Kings," known as a mystical fish with prophetic abilities and a quirky, comedic presence.
  • D. Oki-no-mimi
    Oki-no-mimi is one of the principal peaks forming the summit area of Mount Tanigawa in Japan’s Tanigawa mountain range.
  • E. Yonashiro
    Yonashiro was a former town in Okinawa Prefecture, Japan, that later became part of the city of Uruma through municipal merger.
  • 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_69ca829b45f481909040f7b99a1976ed completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cdb540195881908f25f7dde5c66a75 completed April 2, 2026, 12:16 a.m.
NED1 Entity disambiguation (via context triple) batch_69e4f31e3d5c8190a044eaf67ebc9f08 completed April 19, 2026, 3:22 p.m.
Created at: March 30, 2026, 8:42 p.m.