Triple

T28452236
Position Surface form Disambiguated ID Type / Status
Subject London Hospital Medical College E716607 entity
Predicate affiliatedWith P254 FINISHED
Object The London Hospital
The London Hospital is a major historic teaching hospital in London that has long served as a key center for medical education and patient care.
 E1870751 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: The London Hospital | Statement: [London Hospital Medical College, affiliatedWith, The London Hospital]
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: The London Hospital
Triple: [London Hospital Medical College, affiliatedWith, The London Hospital]
Generated description
The London Hospital is a major historic teaching hospital in London that has long served as a key center for medical education and patient care.

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_69efd6b76f8c8190a7ba908aca280942 completed April 27, 2026, 9:35 p.m.
NER Named-entity recognition batch_69f64e72b5208190869d49fcb3f0a546 completed May 2, 2026, 7:20 p.m.
NED1 Entity disambiguation (via context triple) batch_6a260bf7b0d48190b3d5db47d2b7fa85 completed June 8, 2026, 12:25 a.m.
NEDg Description generation batch_6a261053b87881909e66525205c2de35 completed June 8, 2026, 12:44 a.m.
NED2 Entity disambiguation (via description) batch_6a26140caa84819098a28ce1ff918e72 completed June 8, 2026, 12:59 a.m.
Created at: April 28, 2026, 1:52 a.m.