Triple

T1396099
Position Surface form Disambiguated ID Type / Status
Subject Roman Herzog E30668 entity
Predicate placeOfDeath P21 FINISHED
Object Jena E60682 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: Jena | Statement: [Roman Herzog, placeOfDeath, Jena]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jena
Context triple: [Roman Herzog, placeOfDeath, Jena]
  • A. Jena chosen
    Jena is a historic university city in the German state of Thuringia, known for its role in optics, philosophy, and science.
  • B. Gotha
    Gotha is a historic German city in Thuringia known for its former ducal court, cultural heritage, and role as a residence of various German noble houses.
  • C. Neustrelitz
    Neustrelitz is a town in northeastern Germany known for hosting a key research center of the German Aerospace Center (DLR), particularly focused on satellite data and space-related technologies.
  • D. Leipzig
    Leipzig is a major city in eastern Germany known for its rich cultural heritage, vibrant music and arts scene, and important role in trade and commerce.
  • E. Regensburg
    Regensburg is a historic city in southeastern Germany known for its well-preserved medieval old town on the Danube River.
  • 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_69a498fd4e408190bd73eca30ea9754c completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c37fd6e0819084d610ef041db3af completed March 1, 2026, 10:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69af6523d8f4819082396925561acdc4 completed March 10, 2026, 12:26 a.m.
Created at: March 1, 2026, 7:59 p.m.