Triple

T27268558
Position Surface form Disambiguated ID Type / Status
Subject Gwilym E687979 entity
Predicate hasNotableBearer P458 FINISHED
Object Gwilym Iwan Jones
Gwilym Iwan Jones was a Welsh photographer and anthropologist best known for his extensive visual and ethnographic documentation of Nigerian cultures in the mid-20th century.
 E1774116 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: Gwilym Iwan Jones | Statement: [Gwilym, hasNotableBearer, Gwilym Iwan Jones]
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: Gwilym Iwan Jones
Triple: [Gwilym, hasNotableBearer, Gwilym Iwan Jones]
Generated description
Gwilym Iwan Jones was a Welsh photographer and anthropologist best known for his extensive visual and ethnographic documentation of Nigerian cultures in the mid-20th century.

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_69ef3557abc481908bf3c146f0f3356a completed April 27, 2026, 10:07 a.m.
NER Named-entity recognition batch_69f626f529548190ba59773be80922a0 completed May 2, 2026, 4:31 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12bbc3eab48190a806fe756db15d23 completed May 24, 2026, 8:50 a.m.
NEDg Description generation batch_6a12bc5e92b08190a2a7f60630f6d0ff completed May 24, 2026, 8:52 a.m.
NED2 Entity disambiguation (via description) batch_6a12bcd0c164819098f637afcad01642 completed May 24, 2026, 8:54 a.m.
Created at: April 27, 2026, 10:57 a.m.