Triple

T1298327
Position Surface form Disambiguated ID Type / Status
Subject CHF E27703 entity
Predicate legalTenderIn P188 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: [CHF, legalTenderIn, Liechtenstein]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Liechtenstein
Context triple: [CHF, legalTenderIn, 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. Lichtenstein
    Lichtenstein is a surname most famously associated with Roy Lichtenstein, the American pop artist known for his comic-strip-inspired paintings.
  • D. 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.
  • E. 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.
  • 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_69a496d6682881909ba658f1c1e0e2b0 completed March 1, 2026, 7:43 p.m.
NER Named-entity recognition batch_69a4c111a76c81909e6b914694986251 completed March 1, 2026, 10:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad293872cc8190894581cff289627e completed March 8, 2026, 7:46 a.m.
Created at: March 1, 2026, 7:51 p.m.