Triple

T21091069
Position Surface form Disambiguated ID Type / Status
Subject Great Missenden E519637 entity
Predicate hasRoad P959 FINISHED
Object A413 road
The A413 road is a primary route in England that runs through Buckinghamshire and surrounding counties, linking towns such as Aylesbury, Amersham, and Great Missenden with the wider road network.
E2291313 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: A413 road | Statement: [Great Missenden, hasRoad, A413 road]
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: A413 road
Triple: [Great Missenden, hasRoad, A413 road]
Generated description
The A413 road is a primary route in England that runs through Buckinghamshire and surrounding counties, linking towns such as Aylesbury, Amersham, and Great Missenden with the wider road network.

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_69e0b507dd9081908fb8bfcbef4c8b46 completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e7094ea7f881909db83bf6961b41ec completed April 21, 2026, 5:21 a.m.
NED1 Entity disambiguation (via context triple) batch_6a5c4919d4a08190a7bb8ff08dbcb4a2 completed July 19, 2026, 3:48 a.m.
NEDg Description generation batch_6a5c49ead6748190bffce60b24716acb completed July 19, 2026, 3:52 a.m.
NED2 Entity disambiguation (via description) batch_6a5c4aa384e88190bf48e076738c0b07 completed July 19, 2026, 3:55 a.m.
Created at: April 16, 2026, 2:51 p.m.