Triple

T28814924
Position Surface form Disambiguated ID Type / Status
Subject Battle of Thurles E727615 entity
Predicate hasCategory P87 FINISHED
Object History of County Tipperary
History of County Tipperary encompasses the political, social, and military developments of this Irish county from ancient times through medieval conflicts and modern transformations.
E1834537 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: History of County Tipperary | Statement: [Battle of Thurles, hasCategory, History of County Tipperary]
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: History of County Tipperary
Triple: [Battle of Thurles, hasCategory, History of County Tipperary]
Generated description
History of County Tipperary encompasses the political, social, and military developments of this Irish county from ancient times through medieval conflicts and modern transformations.

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_69f0319d09088190bbf14cdf1987792a completed April 28, 2026, 4:03 a.m.
NER Named-entity recognition batch_69f658f270d88190a183a3eda5c649ff completed May 2, 2026, 8:05 p.m.
NED1 Entity disambiguation (via context triple) batch_6a24a2845e1081909df8576a4b8c32c5 completed June 6, 2026, 10:43 p.m.
NEDg Description generation batch_6a24a6a782cc819088610383db44f5af completed June 6, 2026, 11 p.m.
NED2 Entity disambiguation (via description) batch_6a24aab9053081909350507082946a76 completed June 6, 2026, 11:18 p.m.
Created at: April 28, 2026, 6:32 a.m.