Triple

T24446731
Position Surface form Disambiguated ID Type / Status
Subject Alberto Campo Baeza E616423 entity
Predicate awardReceived P11 FINISHED
Object Arnold W. Brunner Memorial Prize
The Arnold W. Brunner Memorial Prize is a prestigious architecture award presented by the American Academy of Arts and Letters to honor significant contributions to the field.
 E1636562 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: Arnold W. Brunner Memorial Prize | Statement: [Alberto Campo Baeza, awardReceived, Arnold W. Brunner Memorial Prize]
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: Arnold W. Brunner Memorial Prize
Triple: [Alberto Campo Baeza, awardReceived, Arnold W. Brunner Memorial Prize]
Generated description
The Arnold W. Brunner Memorial Prize is a prestigious architecture award presented by the American Academy of Arts and Letters to honor significant contributions to the field.

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_69e2d7edca608190aafefc8877a1b4da completed April 18, 2026, 1:01 a.m.
NER Named-entity recognition batch_69f29853a3ac81908bb5398539a44cd6 completed April 29, 2026, 11:46 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0fe383163c819094ee1df1477e37e9 completed May 22, 2026, 5:02 a.m.
NEDg Description generation batch_6a0fe47cbaf881909fbc9d3f0d2e99c1 completed May 22, 2026, 5:07 a.m.
NED2 Entity disambiguation (via description) batch_6a0fe523e5648190bde36809c67adb54 completed May 22, 2026, 5:09 a.m.
Created at: April 18, 2026, 2:17 a.m.