Triple

T38375001
Position Surface form Disambiguated ID Type / Status
Subject Plateau of Herve E893598 entity
Predicate namedAfter P63 FINISHED
Object town of Herve
The town of Herve is a municipality in the province of Liège in eastern Belgium, historically known for its dairy production and giving its name to the surrounding Plateau of Herve region.
E2267774 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 Herve | Statement: [Plateau of Herve, namedAfter, town of Herve]
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 Herve
Triple: [Plateau of Herve, namedAfter, town of Herve]
Generated description
The town of Herve is a municipality in the province of Liège in eastern Belgium, historically known for its dairy production and giving its name to the surrounding Plateau of Herve region.

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_69f76e4b1f748190a380696a16eae4a2 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69fcccfa03488190891b06c0ecf215e0 completed May 7, 2026, 5:33 p.m.
NED1 Entity disambiguation (via context triple) batch_6a41b2a0e1a481908f9d7f257a7bad2b completed June 28, 2026, 11:47 p.m.
NEDg Description generation batch_6a41b3aad0308190a8b1aea3b38ddc18 completed June 28, 2026, 11:52 p.m.
NED2 Entity disambiguation (via description) batch_6a41b4aff75081909d0946a1c0992447 completed June 28, 2026, 11:56 p.m.
Created at: May 3, 2026, 4:31 p.m.