Triple

T38568900
Position Surface form Disambiguated ID Type / Status
Subject Sir Michael Stoker E928306 entity
Predicate knownAs P39 FINISHED
Object Sir Michael Stoker, British virologist
Sir Michael Stoker was a prominent British virologist noted for his pioneering research in viral oncology and his leadership roles in advancing medical and biological sciences in the UK.
 E2275420 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: Sir Michael Stoker, British virologist | Statement: [Sir Michael Stoker, knownAs, Sir Michael Stoker, British virologist]
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: Sir Michael Stoker, British virologist
Triple: [Sir Michael Stoker, knownAs, Sir Michael Stoker, British virologist]
Generated description
Sir Michael Stoker was a prominent British virologist noted for his pioneering research in viral oncology and his leadership roles in advancing medical and biological sciences in the UK.

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_69f76eb8d1808190a588af29d8b266d6 completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fcd90c33c481908a932edf6adbe5c6 completed May 7, 2026, 6:25 p.m.
NED1 Entity disambiguation (via context triple) batch_6a41e04a1da081908a114568df484875 completed June 29, 2026, 3:02 a.m.
NEDg Description generation batch_6a41e41c47a4819080aad7cc077b3210 completed June 29, 2026, 3:18 a.m.
NED2 Entity disambiguation (via description) batch_6a41e4bcdaac8190b53381cc8934bfab completed June 29, 2026, 3:21 a.m.
Created at: May 3, 2026, 4:32 p.m.