Triple

T4081082
Position Surface form Disambiguated ID Type / Status
Subject Jabir ibn Hayyan E87478 entity
Predicate name P16 FINISHED
Object Geber
Geber is the Latinized name of Jabir ibn Hayyan, a pioneering medieval Islamic alchemist often regarded as the father of early chemistry.
 E410934 NE FINISHED

How this triple was built (4 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: Geber | Statement: [Jabir ibn Hayyan, name, Geber]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Geber
Context triple: [Jabir ibn Hayyan, name, Geber]
  • A. Gsell
    Gsell is a surname of Germanic origin borne by various notable individuals, including artists, scholars, and public figures.
  • B. Gemer
    Gemer is a historical and geographical region in southern Slovakia known for its mining heritage, medieval castles, and karst landscapes.
  • C. Gweru
    Gweru is a central Zimbabwean city that serves as the capital of the Midlands Province and an important commercial and transportation hub.
  • D. Paible
    Paible is a small coastal settlement on the island of North Uist in Scotland’s Outer Hebrides.
  • E. Gedo
    Gedo is a region in southwestern Somalia known for its strategic location bordering Kenya and Ethiopia and its role within the federal state of Jubaland.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Geber
Triple: [Jabir ibn Hayyan, name, Geber]
Generated description
Geber is the Latinized name of Jabir ibn Hayyan, a pioneering medieval Islamic alchemist often regarded as the father of early chemistry.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Geber
Target entity description: Geber is the Latinized name of Jabir ibn Hayyan, a pioneering medieval Islamic alchemist often regarded as the father of early chemistry.
  • A. Gsell
    Gsell is a surname of Germanic origin borne by various notable individuals, including artists, scholars, and public figures.
  • B. Gemer
    Gemer is a historical and geographical region in southern Slovakia known for its mining heritage, medieval castles, and karst landscapes.
  • C. Gweru
    Gweru is a central Zimbabwean city that serves as the capital of the Midlands Province and an important commercial and transportation hub.
  • D. Paible
    Paible is a small coastal settlement on the island of North Uist in Scotland’s Outer Hebrides.
  • E. Gedo
    Gedo is a region in southwestern Somalia known for its strategic location bordering Kenya and Ethiopia and its role within the federal state of Jubaland.
  • F. None of above. chosen

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_69aed9435cf48190ad1da737c962d19d completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefc5204d881909829de15015aa50d completed March 9, 2026, 4:58 p.m.
NED1 Entity disambiguation (via context triple) batch_69b562c3e1b081908cd783d9399a751f completed March 14, 2026, 1:29 p.m.
NEDg Description generation batch_69b5647cbad48190935cb9bdde3f6af0 completed March 14, 2026, 1:37 p.m.
NED2 Entity disambiguation (via description) batch_69b564e930e48190b8b95d3cae65dbd2 completed March 14, 2026, 1:38 p.m.
Created at: March 9, 2026, 3:39 p.m.