Triple

T29394549
Position Surface form Disambiguated ID Type / Status
Subject Burnchurch, County Kilkenny, Ireland E745458 entity
Predicate hasNotableSite P2462 FINISHED
Object Burnchurch Castle
Burnchurch Castle is a well-preserved medieval tower house in County Kilkenny, Ireland, noted for its distinctive architecture and historical significance.
E1888539 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: Burnchurch Castle | Statement: [Burnchurch, County Kilkenny, Ireland, hasNotableSite, Burnchurch Castle]
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: Burnchurch Castle
Triple: [Burnchurch, County Kilkenny, Ireland, hasNotableSite, Burnchurch Castle]
Generated description
Burnchurch Castle is a well-preserved medieval tower house in County Kilkenny, Ireland, noted for its distinctive architecture and historical significance.

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_69f0a79dfabc81908755382ee47791e2 completed April 28, 2026, 12:27 p.m.
NER Named-entity recognition batch_69f66a00ecb08190bfa4a276a164620d completed May 2, 2026, 9:17 p.m.
NED1 Entity disambiguation (via context triple) batch_6a26f1a68ae881909e6af32d0c9dbde2 completed June 8, 2026, 4:45 p.m.
NEDg Description generation batch_6a26f2a076d4819086a7a4bf85946196 completed June 8, 2026, 4:49 p.m.
NED2 Entity disambiguation (via description) batch_6a26f34f9b488190b90e3e36cf7174dc completed June 8, 2026, 4:52 p.m.
Created at: April 28, 2026, 2:45 p.m.