Triple

T34698296
Position Surface form Disambiguated ID Type / Status
Subject parish church of Goslar market quarter E891092 entity
Predicate locatedIn P40 FINISHED
Object Old Town of Goslar
The Old Town of Goslar is a well-preserved medieval German town and UNESCO World Heritage Site renowned for its historic architecture, narrow streets, and rich mining-era heritage.
E2108793 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: Old Town of Goslar | Statement: [parish church of Goslar market quarter, locatedIn, Old Town of Goslar]
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: Old Town of Goslar
Triple: [parish church of Goslar market quarter, locatedIn, Old Town of Goslar]
Generated description
The Old Town of Goslar is a well-preserved medieval German town and UNESCO World Heritage Site renowned for its historic architecture, narrow streets, and rich mining-era heritage.

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_69f349db7ab8819086808e833f472871 completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69f7238172748190b8cd340ad1f4ba80 completed May 3, 2026, 10:29 a.m.
NED1 Entity disambiguation (via context triple) batch_6a375bd93d508190afd083c13e996512 completed June 21, 2026, 3:34 a.m.
NEDg Description generation batch_6a375c4ae6208190ab58e5eaff4a0eb0 completed June 21, 2026, 3:36 a.m.
NED2 Entity disambiguation (via description) batch_6a375ca5242081909ead70636511df76 completed June 21, 2026, 3:38 a.m.
Created at: May 1, 2026, 2:05 a.m.