Triple

T24945583
Position Surface form Disambiguated ID Type / Status
Subject Victorian gold rush E624177 entity
Predicate hasPart P35 FINISHED
Object Maldon goldfields
The Maldon goldfields are a historic gold-mining district in central Victoria, Australia, renowned for their rich 19th-century deposits and well-preserved heritage town.
E1660616 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: Maldon goldfields | Statement: [Victorian gold rush, hasPart, Maldon goldfields]
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: Maldon goldfields
Triple: [Victorian gold rush, hasPart, Maldon goldfields]
Generated description
The Maldon goldfields are a historic gold-mining district in central Victoria, Australia, renowned for their rich 19th-century deposits and well-preserved heritage town.

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_69e2ff22e4c48190a0444b5a044f14e8 completed April 18, 2026, 3:48 a.m.
NER Named-entity recognition batch_69f423de8280819099a06b9bbf2a37e9 completed May 1, 2026, 3:54 a.m.
NED1 Entity disambiguation (via context triple) batch_6a10489fdba481908e3f14f1fa2e98ae completed May 22, 2026, 12:14 p.m.
NEDg Description generation batch_6a1049d7d4bc819081cf52476b0c0a1d completed May 22, 2026, 12:19 p.m.
NED2 Entity disambiguation (via description) batch_6a104a50e59c81908e576aeb2cebc1c5 completed May 22, 2026, 12:21 p.m.
Created at: April 18, 2026, 5:54 a.m.