Triple

T28761573
Position Surface form Disambiguated ID Type / Status
Subject Stanfield Organization E726127 entity
Predicate hasMember P10 FINISHED
Object Felicia Snoop Pearson
Felicia "Snoop" Pearson is a fictional enforcer and hitwoman for the Stanfield drug organization in the television series "The Wire," portrayed by actress Felicia Pearson.
E1833745 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: Felicia Snoop Pearson | Statement: [Stanfield Organization, hasMember, Felicia Snoop Pearson]
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: Felicia Snoop Pearson
Triple: [Stanfield Organization, hasMember, Felicia Snoop Pearson]
Generated description
Felicia "Snoop" Pearson is a fictional enforcer and hitwoman for the Stanfield drug organization in the television series "The Wire," portrayed by actress Felicia Pearson.

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_69f03198be14819098fa74e48b3749bf completed April 28, 2026, 4:03 a.m.
NER Named-entity recognition batch_69f657fffee88190b4354875b78e0a7e completed May 2, 2026, 8:01 p.m.
NED1 Entity disambiguation (via context triple) batch_6a24a25d64c4819087213e5c37c56a54 completed June 6, 2026, 10:42 p.m.
NEDg Description generation batch_6a24a667d6d08190917858826b13e134 completed June 6, 2026, 10:59 p.m.
NED2 Entity disambiguation (via description) batch_6a24aaa014a081908831ccb241673fae completed June 6, 2026, 11:17 p.m.
Created at: April 28, 2026, 6:12 a.m.