Triple

T2669428
Position Surface form Disambiguated ID Type / Status
Subject Law French E55713 entity
Predicate hasAlternativeName P39 FINISHED
Object Legal French E55713 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: Legal French | Statement: [Law French, hasAlternativeName, Legal French]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Legal French
Context triple: [Law French, hasAlternativeName, Legal French]
  • A. Law French chosen
    Law French is a specialized dialect of Anglo-Norman historically used in English legal proceedings, court records, and legal terminology.
  • B. French law
    French law is the civil law-based legal system of France that governs public and private life through codified statutes, regulations, and judicial interpretation.
  • C. Guernsey Legal French
    Guernsey Legal French is a specialized variety of French used historically in the legal system of Guernsey, preserving many archaic Norman and Anglo-Norman features.
  • D. French
    French is a Romance language that evolved from Latin and is now spoken worldwide as both a native and official language in many countries.
  • E. French American
    French Americans are U.S. residents or citizens of French ancestry, including both descendants of early French settlers and more recent immigrants from France.
  • 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_69ab49e54de48190be708cd1cf8be073 completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abd98d32ac8190b8edd9421b706532 completed March 7, 2026, 7:53 a.m.
NED1 Entity disambiguation (via context triple) batch_69afa05d028c8190860587da07ea7e9b completed March 10, 2026, 4:38 a.m.
Created at: March 6, 2026, 9:54 p.m.