Triple

T143134
Position Surface form Disambiguated ID Type / Status
Subject Austria E2895 entity
Predicate borderCountry P224 FINISHED
Object Liechtenstein E19647 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: Liechtenstein | Statement: [Austria, borderCountry, Liechtenstein]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Liechtenstein
Context triple: [Austria, borderCountry, Liechtenstein]
  • A. Liechtenstein chosen
    Liechtenstein is a small, landlocked principality in Central Europe known for its alpine landscape, strong financial sector, and status as one of the world's wealthiest countries per capita.
  • B. Luxembourg
    Luxembourg is a small, landlocked Western European country known for its prosperous economy, status as a major financial center, and role as a founding member of the European Union.
  • C. Andorra
    Andorra is a small, landlocked principality in the eastern Pyrenees between France and Spain, known for its mountainous terrain, tourism, and status as a tax haven.
  • D. Monaco
    Monaco is a small sovereign city-state on the French Riviera known for its wealth, luxury tourism, and status as a major tax haven and gambling hub.
  • E. Switzerland
    Switzerland is a landlocked Central European country known for its long-standing neutrality, mountainous landscapes, financial centers, and multilingual, federal political system.
  • 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_69a2521e35c08190b28e5c9f1e3c9b59 completed Feb. 28, 2026, 2:25 a.m.
NER Named-entity recognition batch_69a257caf678819092e975d5167f9df4 completed Feb. 28, 2026, 2:49 a.m.
NED1 Entity disambiguation (via context triple) batch_69a3dd28b4008190bce7ea85b1c9988e completed March 1, 2026, 6:31 a.m.
Created at: Feb. 28, 2026, 2:31 a.m.