Triple

T37157416
Position Surface form Disambiguated ID Type / Status
Subject Baron Auckland E920546 entity
Predicate namedAfter P63 FINISHED
Object Auckland, County Durham
Auckland, County Durham is a historic town in northeast England, known for its medieval heritage and as the namesake of the Auckland peerage title.
E2216119 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: Auckland, County Durham | Statement: [Baron Auckland, namedAfter, Auckland, County Durham]
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: Auckland, County Durham
Triple: [Baron Auckland, namedAfter, Auckland, County Durham]
Generated description
Auckland, County Durham is a historic town in northeast England, known for its medieval heritage and as the namesake of the Auckland peerage title.

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_69f76ea0429081908c711b55599eac3c completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69fb3091f37c819096779270a8825aee completed May 6, 2026, 12:14 p.m.
NED1 Entity disambiguation (via context triple) batch_6a402bb633908190ab3e1af6071db5e5 completed June 27, 2026, 7:59 p.m.
NEDg Description generation batch_6a402c23c4e481908e808481ba088b5f completed June 27, 2026, 8:01 p.m.
NED2 Entity disambiguation (via description) batch_6a40304ade9881909cdbe86d75532576 completed June 27, 2026, 8:19 p.m.
Created at: May 3, 2026, 4:15 p.m.