Triple

T24439759
Position Surface form Disambiguated ID Type / Status
Subject Weigel E616225 entity
Predicate hasNotableBearer P458 FINISHED
Object Christian Ehrenfried Weigel
Christian Ehrenfried Weigel was an 18th–19th century German chemist and botanist known for his contributions to mineral analysis and for having several plant species named in his honor.
E1643725 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: Christian Ehrenfried Weigel | Statement: [Weigel, hasNotableBearer, Christian Ehrenfried Weigel]
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: Christian Ehrenfried Weigel
Triple: [Weigel, hasNotableBearer, Christian Ehrenfried Weigel]
Generated description
Christian Ehrenfried Weigel was an 18th–19th century German chemist and botanist known for his contributions to mineral analysis and for having several plant species named in his honor.

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_69e2d7ec44b081909ccaf1f3bbec0641 completed April 18, 2026, 1:01 a.m.
NER Named-entity recognition batch_69f2978a5cac81909d2141b606714fd4 completed April 29, 2026, 11:43 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10046304d08190be1561847970edcf completed May 22, 2026, 7:23 a.m.
NEDg Description generation batch_6a10060d1ab081909d164eaee17906dd completed May 22, 2026, 7:30 a.m.
NED2 Entity disambiguation (via description) batch_6a10068e201081909510138c6caf24fa completed May 22, 2026, 7:32 a.m.
Created at: April 18, 2026, 2:17 a.m.