Triple

T30923504
Position Surface form Disambiguated ID Type / Status
Subject Brabantine stuiver E787791 entity
Predicate relatedCurrency P245 FINISHED
Object Brabantine penny
The Brabantine penny was a small medieval and early modern silver coin used in the Duchy of Brabant and surrounding Low Countries regions as a basic unit of currency.
E1943901 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: Brabantine penny | Statement: [Brabantine stuiver, relatedCurrency, Brabantine penny]
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: Brabantine penny
Triple: [Brabantine stuiver, relatedCurrency, Brabantine penny]
Generated description
The Brabantine penny was a small medieval and early modern silver coin used in the Duchy of Brabant and surrounding Low Countries regions as a basic unit of currency.

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_69f224bfaca88190b9d0dfcc86297fe9 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f692b76a588190bde7401df721f418 completed May 3, 2026, 12:11 a.m.
NED1 Entity disambiguation (via context triple) batch_6a292afac7248190b3648ef6cff6db9e completed June 10, 2026, 9:14 a.m.
NEDg Description generation batch_6a292bba50988190872ce52d274d9ddf completed June 10, 2026, 9:17 a.m.
NED2 Entity disambiguation (via description) batch_6a292c75ef8481908b7b700acfc11de5 completed June 10, 2026, 9:20 a.m.
Created at: April 29, 2026, 8:51 p.m.