Triple

T58934
Position Surface form Disambiguated ID Type / Status
Subject Claude E1167 entity
Predicate hasLanguage P15 FINISHED
Object Romanian E2736 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: Romanian | Statement: [Claude, hasLanguage, Romanian]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Romanian
Context triple: [Claude, hasLanguage, Romanian]
  • A. Romanian language chosen
    Romanian is a Romance language spoken primarily in Romania and Moldova, notable for preserving many features of Latin while incorporating significant Slavic and Balkan influences.
  • B. Polish language
    Polish language is a West Slavic language spoken primarily in Poland and by Polish communities worldwide, written in the Latin alphabet with distinctive diacritics.
  • C. Russian language
    Russian is an East Slavic language spoken primarily in Russia and neighboring countries, serving as one of the world's major languages in politics, science, and culture.
  • D. Turkish language
    Turkish is a Turkic language primarily spoken in Turkey and Cyprus, known for its vowel harmony, agglutinative grammar, and modern standard form established after Atatürk’s language reforms.
  • E. Esperanto
    Esperanto is a constructed international auxiliary language created in the late 19th century to facilitate easy and politically neutral communication between speakers of different native languages.
  • 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_69a24a552ef88190a0df287d68c65cba completed Feb. 28, 2026, 1:52 a.m.
NER Named-entity recognition batch_69a24ec5f46081909f3ba0b25190282b completed Feb. 28, 2026, 2:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69a2554a9e70819099ab14df3da5e403 completed Feb. 28, 2026, 2:39 a.m.
Created at: Feb. 28, 2026, 1:55 a.m.