Triple

T2611944
Position Surface form Disambiguated ID Type / Status
Subject Maria Elisabeth Lämmerhirt E58793 entity
Predicate givenName P17 FINISHED
Object Elisabeth E113408 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: Elisabeth | Statement: [Maria Elisabeth Lämmerhirt, givenName, Elisabeth]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Elisabeth
Context triple: [Maria Elisabeth Lämmerhirt, givenName, Elisabeth]
  • A. Elisabeth chosen
    Elisabeth is a feminine given name of Hebrew origin, commonly used in various European languages as a form of Elizabeth.
  • B. Louise of Great Britain
    Louise of Great Britain was a British princess who became Queen of Denmark and Norway through her marriage to King Frederick V.
  • C. Anne
    Anne is the given name of Anne Morrow Lindbergh, the American author and aviator who was married to famed aviator Charles Lindbergh.
  • D. Anne
    Anne is a female given name of Hebrew origin, commonly used in many European languages and historically borne by numerous queens, saints, and notable women.
  • E. Anne
    Anne is traditionally revered in Christian tradition as the mother of the Virgin Mary and the grandmother of Jesus.
  • 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_69ab4ac444dc819099614e534dd6021f completed March 6, 2026, 9:44 p.m.
NER Named-entity recognition batch_69abd87c5fec8190a428b94b90265352 completed March 7, 2026, 7:49 a.m.
NED1 Entity disambiguation (via context triple) batch_69af83e816808190aa2c91801fd3c6c7 completed March 10, 2026, 2:37 a.m.
Created at: March 6, 2026, 9:50 p.m.