Triple

T20409636
Position Surface form Disambiguated ID Type / Status
Subject Frantic E500554 entity
Predicate hasPart P35 FINISHED
Object San Simeon NE NERFINISHED

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: San Simeon | Statement: [Frantic, hasPart, San Simeon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: San Simeon
Context triple: [Frantic, hasPart, San Simeon]
  • A. San Simeon chosen
    San Simeon is a small coastal community in California best known as the home of Hearst Castle and scenic stretches of the Pacific coastline.
  • B. Solomon Luna
    Solomon Luna was a prominent New Mexican rancher, banker, and Republican politician of the late 19th and early 20th centuries, influential in the territory’s economic and political development.
  • C. Padre Garcia
    Padre Garcia is a landlocked agricultural municipality in the province of Batangas in the Philippines, known particularly for its thriving livestock market.
  • D. San Simon
    San Simon is a municipality in the province of Pampanga in the Philippines, known for its agricultural economy and proximity to major urban centers in Central Luzon.
  • E. San Policarpo
    San Policarpo is a coastal municipality in the province of Eastern Samar in the Philippines, known for its fishing communities and exposure to Pacific typhoons.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4a935588190b9446a99b37ced44 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e67a3e0c1c8190be39d7f09c839dfa completed April 20, 2026, 7:10 p.m.
Created at: April 16, 2026, 11:29 a.m.