Triple

T34234769
Position Surface form Disambiguated ID Type / Status
Subject Papworth Hospital E878302 entity
Predicate formerName P65 FINISHED
Object Cambridgeshire Tuberculosis Colony
Cambridgeshire Tuberculosis Colony was an early 20th-century British medical settlement established to treat and rehabilitate tuberculosis patients in a village-style community setting.
E2088612 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: Cambridgeshire Tuberculosis Colony | Statement: [Papworth Hospital, formerName, Cambridgeshire Tuberculosis Colony]
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: Cambridgeshire Tuberculosis Colony
Triple: [Papworth Hospital, formerName, Cambridgeshire Tuberculosis Colony]
Generated description
Cambridgeshire Tuberculosis Colony was an early 20th-century British medical settlement established to treat and rehabilitate tuberculosis patients in a village-style community setting.

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_69f349b22d8c819096b22df268382aa9 completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69f710b492f481908ee1b0a76011733c completed May 3, 2026, 9:09 a.m.
NED1 Entity disambiguation (via context triple) batch_6a36d5e68f7c8190bb05f40630102aac completed June 20, 2026, 6:03 p.m.
NEDg Description generation batch_6a36ded74ec88190b2d25a00f2b30bc8 completed June 20, 2026, 6:41 p.m.
NED2 Entity disambiguation (via description) batch_6a36e13bdf9c81908161cecded7bd29b completed June 20, 2026, 6:51 p.m.
Created at: May 1, 2026, 1:56 a.m.