Triple

T24845679
Position Surface form Disambiguated ID Type / Status
Subject Penrose family E621739 entity
Predicate hasNotableMember P304 FINISHED
Object Francis Penrose
Francis Penrose was a 19th-century English architect and archaeologist best known for his work on the restoration and precise measurement of the Parthenon in Athens.
 E1662709 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: Francis Penrose | Statement: [Penrose family, hasNotableMember, Francis Penrose]
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: Francis Penrose
Triple: [Penrose family, hasNotableMember, Francis Penrose]
Generated description
Francis Penrose was a 19th-century English architect and archaeologist best known for his work on the restoration and precise measurement of the Parthenon in Athens.

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_69e2fac297e481909d3aedc75f585e42 completed April 18, 2026, 3:30 a.m.
NER Named-entity recognition batch_69f422cd95e481908eb2982571403b4e completed May 1, 2026, 3:49 a.m.
NED1 Entity disambiguation (via context triple) batch_6a10488c8ea48190a1fd6f4f642eb1a4 completed May 22, 2026, 12:14 p.m.
NEDg Description generation batch_6a1049f04c5c819091dc7e9d760c91ce completed May 22, 2026, 12:20 p.m.
NED2 Entity disambiguation (via description) batch_6a104bbb9b6c81908fcc21c8c027b9de completed May 22, 2026, 12:27 p.m.
Created at: April 18, 2026, 5:19 a.m.