Triple

T3935669
Position Surface form Disambiguated ID Type / Status
Subject Lefkada E90903 entity
Predicate nearCity P350 FINISHED
Object Preveza E98737 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: Preveza | Statement: [Lefkada, nearCity, Preveza]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Preveza
Context triple: [Lefkada, nearCity, Preveza]
  • A. Preveza chosen
    Preveza is a coastal city in northwestern Greece known for its strategic location at the entrance of the Ambracian Gulf, its historic old town, and nearby beaches.
  • B. Rissani
    Rissani is a historic town in eastern Morocco, known as a gateway to the Sahara Desert and an important former caravan and trading center.
  • C. Monfalcone
    Monfalcone is an industrial port town in northeastern Italy, known for its major shipbuilding industry and location on the Gulf of Trieste.
  • D. Penedono
    Penedono is a small Portuguese municipality in the Viseu district, known for its medieval castle and location within the Douro wine region.
  • E. Morea
    Morea was the medieval name for the Peloponnese peninsula in southern Greece, which served as a significant Byzantine province and later despotate.
  • 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_69aed95f26e0819094b0e71974543a19 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeedcd29148190a98e4549c9ed8888 completed March 9, 2026, 3:57 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5288eb3e481909a68531fd37371a4 completed March 14, 2026, 9:21 a.m.
Created at: March 9, 2026, 3:23 p.m.