Triple

T12594511
Position Surface form Disambiguated ID Type / Status
Subject Center Force E300699 entity
Predicate ship P880 FINISHED
Object Haruna E314408 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: Haruna | Statement: [Center Force, ship, Haruna]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Haruna
Context triple: [Center Force, ship, Haruna]
  • A. Haruna chosen
    Haruna was a Japanese Kongō-class fast battleship that served in the Imperial Japanese Navy during both World Wars and saw extensive action in the Pacific Theater.
  • B. Yahata
    Yahata was a former city in Fukuoka Prefecture, Japan, that became part of the larger city of Kitakyushu through municipal merger.
  • C. Yasu
    Yasu is a Japanese city located in Shiga Prefecture, known for its blend of residential areas, local industry, and proximity to Lake Biwa.
  • D. Sawa
    Sawa is a Japanese surname most prominently associated with Homare Sawa, a legendary Japanese women’s footballer and World Cup winner.
  • E. Takahira
    Takahira was the personal name of Emperor Go-Toba, a late 12th- to early 13th-century Japanese emperor known for his political struggles with the shogunate and his patronage of the arts.
  • 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_69d7bdea2ca881908f379526c13b1145 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d954cde3c0819094e74413d6dcf548 completed April 10, 2026, 7:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69ff677935408190a28af4cd34d82aa4 completed May 9, 2026, 4:57 p.m.
Created at: April 9, 2026, 5:08 p.m.