Triple

T3112764
Position Surface form Disambiguated ID Type / Status
Subject Lineyte-Samarnon E64987 entity
Predicate spokenOnIsland P27873 FINISHED
Object Samar E64988 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: Samar | Statement: [Lineyte-Samarnon, spokenOnIsland, Samar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Samar
Context triple: [Lineyte-Samarnon, spokenOnIsland, Samar]
  • A. Samar
    Samar is a surname most notably associated with Sima Samar, an Afghan human rights advocate, physician, and former minister.
  • B. Samar Island chosen
    Samar Island is a large island in the Eastern Visayas region of the Philippines, known for its rugged terrain, rich biodiversity, and distinct local culture.
  • C. Pomorye
    Pomorye is a historical coastal region of northern Russia along the White Sea, traditionally inhabited by the Pomors and known for maritime trade and exploration.
  • D. Samar Province
    Samar Province is a largely rural island province in the Eastern Visayas region of the Philippines, known for its rugged landscapes, caves, and strong Waray-speaking cultural heritage.
  • E. Leyte
    Leyte is a large island province in the Eastern Visayas region of the Philippines, known for its rich cultural traditions and historical significance, including major World War II events.
  • 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_69ad857eeaf48190b34ebfdaa7a264cf completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada43c79448190aa72f707319e8c5e completed March 8, 2026, 4:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69b3340904b4819097ac23cb2b3fe5d5 completed March 12, 2026, 9:45 p.m.
Created at: March 8, 2026, 3:04 p.m.