Triple

T29202941
Position Surface form Disambiguated ID Type / Status
Subject Svitavy District E740328 entity
Predicate contains P35 FINISHED
Object town of Moravská Třebová
The town of Moravská Třebová is a historic settlement in the Pardubice Region of the Czech Republic, known for its well-preserved Renaissance architecture and former status as an important trade and cultural center.
E1856032 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: town of Moravská Třebová | Statement: [Svitavy District, contains, town of Moravská Třebová]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: town of Moravská Třebová
Triple: [Svitavy District, contains, town of Moravská Třebová]
Generated description
The town of Moravská Třebová is a historic settlement in the Pardubice Region of the Czech Republic, known for its well-preserved Renaissance architecture and former status as an important trade and cultural center.

Provenance (5 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_69f07cb974108190b7e86ca489a6ebb6 completed April 28, 2026, 9:24 a.m.
NER Named-entity recognition batch_69f663c7b4f48190b66f966af570e768 completed May 2, 2026, 8:51 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2569bea46881909c7fc3d2393ee5e3 completed June 7, 2026, 12:53 p.m.
NEDg Description generation batch_6a256dc27c708190b74c697d4eb1f0a2 completed June 7, 2026, 1:10 p.m.
NED2 Entity disambiguation (via description) batch_6a257303ae008190aad081788fc11925 completed June 7, 2026, 1:32 p.m.
Created at: April 28, 2026, 12:07 p.m.