Triple

T28306572
Position Surface form Disambiguated ID Type / Status
Subject Jennifer Aspen E713869 entity
Predicate hasActedIn P15620 FINISHED
Object The Wrong Blind Date
The Wrong Blind Date is a made-for-television thriller film that follows a woman whose attempt at finding love through a blind date spirals into danger and suspense.
 E1811769 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: The Wrong Blind Date | Statement: [Jennifer Aspen, hasActedIn, The Wrong Blind Date]
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: The Wrong Blind Date
Triple: [Jennifer Aspen, hasActedIn, The Wrong Blind Date]
Generated description
The Wrong Blind Date is a made-for-television thriller film that follows a woman whose attempt at finding love through a blind date spirals into danger and suspense.

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_69efb5256afc8190b9322d25c3ae6320 completed April 27, 2026, 7:12 p.m.
NER Named-entity recognition batch_69f644b6b558819095c70a2eb49f1853 completed May 2, 2026, 6:38 p.m.
NED1 Entity disambiguation (via context triple) batch_6a160738dc58819092204bb6841431e7 completed May 26, 2026, 8:48 p.m.
NEDg Description generation batch_6a161433b69c81909fdd10b625bcfb9d completed May 26, 2026, 9:44 p.m.
NED2 Entity disambiguation (via description) batch_6a16149e8dd48190996fb7f0f32fb031 completed May 26, 2026, 9:46 p.m.
Created at: April 27, 2026, 11:38 p.m.