Triple

T14868475
Position Surface form Disambiguated ID Type / Status
Subject Andreas Homoki E349678 entity
Predicate hasDirectedAt P7373 FINISHED
Object Oper Leipzig E735550 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: Oper Leipzig | Statement: [Andreas Homoki, hasDirectedAt, Oper Leipzig]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Oper Leipzig
Context triple: [Andreas Homoki, hasDirectedAt, Oper Leipzig]
  • A. Oper Leipzig chosen
    Oper Leipzig is the main opera company and performance venue in Leipzig, Germany, known for its rich musical tradition and productions of both classic and contemporary works.
  • B. 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.
  • C. Lippendorf
    Lippendorf is a village in Saxony, Germany, historically notable as the birthplace of Katharina von Bora, the wife of Martin Luther.
  • D. 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.
  • E. Leipsoi
    Leipsoi is a small, tranquil Greek island in the Dodecanese known for its traditional villages, clear waters, and unspoiled natural scenery.
  • 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_69d822ee4f408190b6ac3b2fa434f0df completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69ded5776b848190bfe3a06ff261dc31 completed April 15, 2026, 12:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe651067cc8190b9c218ef1f802762 completed May 8, 2026, 10:34 p.m.
Created at: April 10, 2026, 1:55 a.m.