Triple

T10745085
Position Surface form Disambiguated ID Type / Status
Subject Zankle E253429 entity
Predicate locatedOnSiteOf P4380 FINISHED
Object Messina E83961 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: Messina | Statement: [Zankle, locatedOnSiteOf, Messina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Messina
Context triple: [Zankle, locatedOnSiteOf, Messina]
  • A. Messina chosen
    Messina is a major port city in northeastern Sicily, Italy, located on the Strait of Messina opposite mainland Calabria.
  • B. Trapani
    Trapani is a coastal city in western Sicily, Italy, known for its historic port, salt pans, and proximity to the Egadi Islands.
  • C. Palermo
    Palermo is the historic capital of Sicily, renowned for its rich multicultural heritage, including a significant medieval Jewish presence, and its blend of Arab-Norman architecture, vibrant markets, and coastal setting.
  • D. Palermo
    Palermo is a 90 nm, low-power, budget-oriented core used in AMD's Sempron line of processors.
  • E. Palermo
    Palermo is a large, upscale neighborhood in Buenos Aires known for its parks, nightlife, cultural attractions, and trendy dining and shopping areas.
  • 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_69d6aa5e51e8819095f06881cecf152e completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d711b77c4881909d16c6e82a9b86ca completed April 9, 2026, 2:40 a.m.
NED1 Entity disambiguation (via context triple) batch_69deb0a03a1481908edb933b1613a027 completed April 14, 2026, 9:24 p.m.
Created at: April 8, 2026, 9:15 p.m.