Triple

T28672043
Position Surface form Disambiguated ID Type / Status
Subject Hartford Symphony Orchestra E725749 entity
Predicate notableConductor P5559 FINISHED
Object Carolyn Kuan
Carolyn Kuan is an American conductor known for her dynamic leadership and innovative programming, particularly as music director of the Hartford Symphony Orchestra.
E1829735 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: Carolyn Kuan | Statement: [Hartford Symphony Orchestra, notableConductor, Carolyn Kuan]
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: Carolyn Kuan
Triple: [Hartford Symphony Orchestra, notableConductor, Carolyn Kuan]
Generated description
Carolyn Kuan is an American conductor known for her dynamic leadership and innovative programming, particularly as music director of the Hartford Symphony Orchestra.

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_69f01d85be388190b669a0e401e2f2c4 completed April 28, 2026, 2:37 a.m.
NER Named-entity recognition batch_69f65631a7f08190b546f0d035f87f74 completed May 2, 2026, 7:53 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1ccf4257a881908c8dccda94822686 completed June 1, 2026, 12:16 a.m.
NEDg Description generation batch_6a1cd020780c81908d33cd9d1676a762 completed June 1, 2026, 12:19 a.m.
NED2 Entity disambiguation (via description) batch_6a24945efab88190a4ccb8a92331e469 completed June 6, 2026, 9:42 p.m.
Created at: April 28, 2026, 5:04 a.m.