Triple

T34035435
Position Surface form Disambiguated ID Type / Status
Subject Diarmuid E872779 entity
Predicate hasNotableBearer P458 FINISHED
Object Diarmuid O'Hegarty
Diarmuid O'Hegarty was an Irish civil servant and revolutionary who played a key administrative role in the Irish independence movement and the early Irish Free State government.
E2112562 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: Diarmuid O'Hegarty | Statement: [Diarmuid, hasNotableBearer, Diarmuid O'Hegarty]
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: Diarmuid O'Hegarty
Triple: [Diarmuid, hasNotableBearer, Diarmuid O'Hegarty]
Generated description
Diarmuid O'Hegarty was an Irish civil servant and revolutionary who played a key administrative role in the Irish independence movement and the early Irish Free State government.

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_69f349a2527c81909a7cd4bda94d70ad completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f70b3b925c8190a69d8b42a18330f1 completed May 3, 2026, 8:45 a.m.
NED1 Entity disambiguation (via context triple) batch_6a376f86a3908190803a47787eb3bd0a completed June 21, 2026, 4:58 a.m.
NEDg Description generation batch_6a377009741c8190be2010e22fb2dadb completed June 21, 2026, 5 a.m.
NED2 Entity disambiguation (via description) batch_6a37708711608190bc570b03a7c937fe completed June 21, 2026, 5:03 a.m.
Created at: May 1, 2026, 1:51 a.m.