Triple

T35894366
Position Surface form Disambiguated ID Type / Status
Subject Emmenhausen E1038176 entity
Predicate locatedInAdministrativeEntity P40 FINISHED
Object Municipality of Bovenden
The Municipality of Bovenden is a local administrative district in Lower Saxony, Germany, encompassing several villages and communities near the city of Göttingen.
E2160158 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: Municipality of Bovenden | Statement: [Emmenhausen, locatedInAdministrativeEntity, Municipality of Bovenden]
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: Municipality of Bovenden
Triple: [Emmenhausen, locatedInAdministrativeEntity, Municipality of Bovenden]
Generated description
The Municipality of Bovenden is a local administrative district in Lower Saxony, Germany, encompassing several villages and communities near the city of Göttingen.

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_69f76e2190f88190beb2eed798a4ef01 completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f7aa3ce2ac81908545981edf1fafdd completed May 3, 2026, 8:04 p.m.
NED1 Entity disambiguation (via context triple) batch_6a38a4fe50248190b9c498e005d5647b completed June 22, 2026, 2:59 a.m.
NEDg Description generation batch_6a38a5618be48190893b3e8202847748 completed June 22, 2026, 3 a.m.
NED2 Entity disambiguation (via description) batch_6a38a604b0488190a52a2319556ac218 completed June 22, 2026, 3:03 a.m.
Created at: May 3, 2026, 4:06 p.m.