Triple

T24682850
Position Surface form Disambiguated ID Type / Status
Subject Dukho Yeon E611191 entity
Predicate notableWork P4 FINISHED
Object Weill Hall, Cornell University
Weill Hall at Cornell University is a major interdisciplinary research facility primarily dedicated to life sciences and biomedical engineering.
 E1646449 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: Weill Hall, Cornell University | Statement: [Dukho Yeon, notableWork, Weill Hall, Cornell University]
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: Weill Hall, Cornell University
Triple: [Dukho Yeon, notableWork, Weill Hall, Cornell University]
Generated description
Weill Hall at Cornell University is a major interdisciplinary research facility primarily dedicated to life sciences and biomedical engineering.

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_69e2c4d5c2dc8190ac857dea25ec6ce9 completed April 17, 2026, 11:40 p.m.
NER Named-entity recognition batch_69f40fc1aa0c8190999729e8d51aea48 completed May 1, 2026, 2:28 a.m.
NED1 Entity disambiguation (via context triple) batch_6a100ffc3fd081909bcf8805b125240e completed May 22, 2026, 8:12 a.m.
NEDg Description generation batch_6a10136992b481909ee04d5c09867f21 completed May 22, 2026, 8:27 a.m.
NED2 Entity disambiguation (via description) batch_6a10141161b08190b471a7882a4d8aa0 completed May 22, 2026, 8:30 a.m.
Created at: April 18, 2026, 3:16 a.m.