Triple

T8062131
Position Surface form Disambiguated ID Type / Status
Subject Ernest II, Duke of Saxe-Coburg and Gotha E188149 entity
Predicate givenName P17 FINISHED
Object Ernst E81465 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: Ernst | Statement: [Ernest II, Duke of Saxe-Coburg and Gotha, givenName, Ernst]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ernst
Context triple: [Ernest II, Duke of Saxe-Coburg and Gotha, givenName, Ernst]
  • A. Ernst chosen
    Ernst is a masculine given name of Germanic origin, commonly used in German-speaking and Scandinavian countries.
  • B. Günther
    Günther is a German masculine given name traditionally associated with figures of Germanic origin and culture.
  • C. Günther
    Günther is the zoologist who first formally described the impressed tortoise species Manouria impressa.
  • D. Erwin
    Erwin is a masculine given name of German origin, historically associated with figures such as the World War II field marshal Erwin Rommel.
  • E. Hermann
    Hermann is the obsessive, tormented protagonist of Alexander Pushkin’s novella "The Queen of Spades," whose fixation on a secret winning card formula leads to his psychological and moral downfall.
  • 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_69cb3fcd7bcc8190b0a629a813846f13 completed March 31, 2026, 3:30 a.m.
NED1 Entity disambiguation (via context triple) batch_69ccecdfddb08190bfda3bb5c02215d9 completed April 1, 2026, 10:01 a.m.
Created at: March 30, 2026, 5:26 p.m.