Triple

T8060883
Position Surface form Disambiguated ID Type / Status
Subject Lorenz Hart E188116 entity
Predicate givenName P17 FINISHED
Object Lorenz E275141 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: Lorenz | Statement: [Lorenz Hart, givenName, Lorenz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lorenz
Context triple: [Lorenz Hart, givenName, Lorenz]
  • A. Lorenz chosen
    Lorenz is a masculine given name of German origin, historically borne by various notable figures in Europe.
  • B. Lorenz attractor
    The Lorenz attractor is a famous chaotic set arising from a simplified model of atmospheric convection, known for its butterfly-shaped trajectory and role as an early example of deterministic chaos in dynamical systems.
  • C. Rabinovich
    Rabinovich is a Jewish surname of Eastern European origin, notably borne by the Yiddish writer Sholem Aleichem (born Sholem Rabinovich).
  • D. Loren
    Loren is a given name used for people of any gender, often as a variant or shortened form of names like Lorenzo or Lauren.
  • E. Beltrami
    Beltrami is an Italian surname borne by various notable individuals across fields such as music, mathematics, and the arts.
  • 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_69ca82b2f68881908c50560697e210da completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3fcc61c0819085edc26e75c5f6d5 completed March 31, 2026, 3:30 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc63d6484c8190b2fd2c2bef179fc4 completed April 1, 2026, 12:16 a.m.
Created at: March 30, 2026, 5:26 p.m.