Triple

T15276119
Position Surface form Disambiguated ID Type / Status
Subject Carolina E365144 entity
Predicate shortForm P43 FINISHED
Object Lina E757413 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: Lina | Statement: [Carolina, shortForm, Lina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lina
Context triple: [Carolina, shortForm, Lina]
  • A. Lina
    Lina is a Native American servant in Toni Morrison’s novel *A Mercy*, whose history of displacement and resilience reflects the novel’s themes of slavery, colonialism, and survival in 17th-century America.
  • B. Lina
    Lina Heydrich was the wife of high-ranking Nazi official Reinhard Heydrich and a committed supporter of National Socialism in Germany.
  • C. Lina chosen
    Lina is a feminine given name used in various cultures, often as a short form of names like Angelina, Karolina, or Alina.
  • D. Lina
    Lina is one of the four main playable characters in the Japanese video game *Yume Kōjō: Doki Doki Panic*, which later served as the basis for *Super Mario Bros. 2* in the West.
  • E. Lina
    Lina is a female lion character who serves as one of the official mascots for the Japanese professional baseball team Saitama Seibu Lions.
  • 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_69d85a0f08408190b3c3259ae35d79d2 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e00952731c8190bf6a5e6e10c95b94 completed April 15, 2026, 9:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69feef7186d481909067f8088f3ea497 completed May 9, 2026, 8:25 a.m.
Created at: April 10, 2026, 3:14 a.m.