Triple

T32835053
Position Surface form Disambiguated ID Type / Status
Subject Lan Samantha Chang E839801 entity
Predicate awardReceived P11 FINISHED
Object American Academy in Berlin Fellowship
The American Academy in Berlin Fellowship is a prestigious residential program that supports distinguished scholars, writers, and artists with time and resources to pursue their work in Berlin.
 E2026551 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: American Academy in Berlin Fellowship | Statement: [Lan Samantha Chang, awardReceived, American Academy in Berlin Fellowship]
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: American Academy in Berlin Fellowship
Triple: [Lan Samantha Chang, awardReceived, American Academy in Berlin Fellowship]
Generated description
The American Academy in Berlin Fellowship is a prestigious residential program that supports distinguished scholars, writers, and artists with time and resources to pursue their work in Berlin.

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_69f3493f22f88190ae6dd4bc15b6cf8d completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6ce2e90c081908e01d5368bf4497d completed May 3, 2026, 4:25 a.m.
NED1 Entity disambiguation (via context triple) batch_6a34bcf9d9888190be1d2c327db43907 completed June 19, 2026, 3:52 a.m.
NEDg Description generation batch_6a34bdc35a24819088892cb8a675a225 completed June 19, 2026, 3:55 a.m.
NED2 Entity disambiguation (via description) batch_6a34be6eb1808190a6bc47b8d79489ed completed June 19, 2026, 3:58 a.m.
Created at: May 1, 2026, 1:16 a.m.