Triple

T24976187
Position Surface form Disambiguated ID Type / Status
Subject Selwyn College, Cambridge E625027 entity
Predicate hasCommonRoom P4719 FINISHED
Object Middle Combination Room
The Middle Combination Room is the social and academic common room and representative body for postgraduate students at Selwyn College, University of Cambridge.
 E158783 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: Middle Combination Room | Statement: [Selwyn College, Cambridge, hasCommonRoom, Middle Combination Room]
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: Middle Combination Room
Triple: [Selwyn College, Cambridge, hasCommonRoom, Middle Combination Room]
Generated description
The Middle Combination Room is the social and academic common room and representative body for postgraduate students at Selwyn College, University of Cambridge.

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_69e2ff254570819093d197b1900305ac completed April 18, 2026, 3:48 a.m.
NER Named-entity recognition batch_69f4490283c481908c18246dc7125eec completed May 1, 2026, 6:32 a.m.
NED1 Entity disambiguation (via context triple) batch_6a10335d54ec8190811b21160b76d43f completed May 22, 2026, 10:43 a.m.
NEDg Description generation batch_6a103420ed908190b7be8e1a8b82a85c completed May 22, 2026, 10:46 a.m.
NED2 Entity disambiguation (via description) batch_6a1034aa0ed881909d877e1d9159b9d2 completed May 22, 2026, 10:49 a.m.
Created at: April 18, 2026, 6:01 a.m.