Triple

T2903007
Position Surface form Disambiguated ID Type / Status
Subject V. (novel) E62693 entity
Predicate settingLocation P40 FINISHED
Object Malta E9342 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: Malta | Statement: [V. (novel), settingLocation, Malta]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Malta
Context triple: [V. (novel), settingLocation, Malta]
  • A. Malta chosen
    Malta is a small island nation in the central Mediterranean known for its rich history, strategic location, and membership in the European Union.
  • B. Malta
    Malta is a town in Saratoga County, New York, known for its mix of suburban communities, rural landscapes, and the high-tech Luther Forest Technology Campus.
  • C. Żebbuġ, Malta
    Żebbuġ is a historic town in central Malta known for its traditional architecture, parish church, and long-standing cultural and religious festivities.
  • D. San Marino
    San Marino is a small, landlocked microstate surrounded by Italy, known as one of the world’s oldest republics and a popular tourist destination.
  • E. San Marino
    San Marino is a small, affluent residential city in Los Angeles County, California, known for its high-ranking schools and the Huntington Library, Art Museum, and Botanical Gardens.
  • 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_69ab4c3e070c8190b78d3d2c005876dd completed March 6, 2026, 9:50 p.m.
NER Named-entity recognition batch_69abe0b3d20881908cd4f1b465b504af completed March 7, 2026, 8:24 a.m.
NED1 Entity disambiguation (via context triple) batch_69b2033464fc8190b0eeeae9dc7f20b6 completed March 12, 2026, 12:05 a.m.
Created at: March 6, 2026, 10:10 p.m.