Triple

T4429103
Position Surface form Disambiguated ID Type / Status
Subject Luther Forest Technology Campus E95280 entity
Predicate locatedIn P40 FINISHED
Object Malta, New York E95280 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, New York | Statement: [Luther Forest Technology Campus, locatedIn, Malta, New York]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Malta, New York
Context triple: [Luther Forest Technology Campus, locatedIn, Malta, New York]
  • A. Żebbuġ, Malta
    Żebbuġ is a historic town in central Malta known for its traditional architecture, parish church, and long-standing cultural and religious festivities.
  • B. Malta chosen
    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. Malta
    Malta is a small island nation in the central Mediterranean known for its rich history, strategic location, and membership in the European Union.
  • D. New Britain
    New Britain is a large volcanic island in the Bismarck Archipelago of Papua New Guinea, known for its rugged terrain, active volcanoes, and diverse indigenous cultures.
  • E. New Britain
    New Britain is a mid-sized industrial city in central Connecticut known historically for its manufacturing and hardware industries.
  • 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_69b3453c2a0c8190926b574c90766db9 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b35568767c819084d5e18b56a4745e completed March 13, 2026, 12:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69b6136caa248190a84423cede1908c3 completed March 15, 2026, 2:03 a.m.
Created at: March 12, 2026, 11:30 p.m.