Triple

T25188237
Position Surface form Disambiguated ID Type / Status
Subject Medalion Rahimi E630782 entity
Predicate knownFor P22 FINISHED
Object My Dead Boyfriend
"My Dead Boyfriend" is a 2016 dark romantic comedy film about a woman who discovers surprising secrets about her late slacker boyfriend after his sudden death.
 E1667555 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: My Dead Boyfriend | Statement: [Medalion Rahimi, knownFor, My Dead Boyfriend]
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: My Dead Boyfriend
Triple: [Medalion Rahimi, knownFor, My Dead Boyfriend]
Generated description
"My Dead Boyfriend" is a 2016 dark romantic comedy film about a woman who discovers surprising secrets about her late slacker boyfriend after his sudden death.

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_69e75a8a6d088190ba1e82a4345225e7 completed April 21, 2026, 11:07 a.m.
NER Named-entity recognition batch_69f46e0bbd2c8190a30a70dcf5523875 completed May 1, 2026, 9:10 a.m.
NED1 Entity disambiguation (via context triple) batch_6a105d22c5f48190983eb1dca7b52b46 completed May 22, 2026, 1:41 p.m.
NEDg Description generation batch_6a105e52d9fc8190b22dd25b9cec720b completed May 22, 2026, 1:46 p.m.
NED2 Entity disambiguation (via description) batch_6a105ef626c08190933088d575b2e923 completed May 22, 2026, 1:49 p.m.
Created at: April 21, 2026, 12:44 p.m.