Triple

T24300475
Position Surface form Disambiguated ID Type / Status
Subject Franklin Saint E606080 entity
Predicate fictionalUniverse P3758 FINISHED
Object Snowfall universe
The Snowfall universe is the gritty fictional world of the TV crime drama "Snowfall," depicting the rise of the crack cocaine epidemic in 1980s Los Angeles and its impact on characters like Franklin Saint.
E1627383 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: Snowfall universe | Statement: [Franklin Saint, fictionalUniverse, Snowfall universe]
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: Snowfall universe
Triple: [Franklin Saint, fictionalUniverse, Snowfall universe]
Generated description
The Snowfall universe is the gritty fictional world of the TV crime drama "Snowfall," depicting the rise of the crack cocaine epidemic in 1980s Los Angeles and its impact on characters like Franklin Saint.

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_69e29549335881909cbf27adcaba1cf0 completed April 17, 2026, 8:17 p.m.
NER Named-entity recognition batch_69f2915e4ffc8190bf711dae443b3ec1 completed April 29, 2026, 11:16 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0fc9d357608190aeb20950c4b82ab0 completed May 22, 2026, 3:13 a.m.
NEDg Description generation batch_6a0fcb28d7bc81909982e8b806cf5043 completed May 22, 2026, 3:19 a.m.
NED2 Entity disambiguation (via description) batch_6a0fcb9df7d8819084e06c59ca3a5f11 completed May 22, 2026, 3:21 a.m.
Created at: April 18, 2026, 12:09 a.m.