Triple

T32619969
Position Surface form Disambiguated ID Type / Status
Subject The Chicago Code E833894 entity
Predicate hasMainCharacter P1183 FINISHED
Object Teresa Colvin
Teresa Colvin is the determined and principled Chicago police superintendent who leads the fight against corruption in the television drama "The Chicago Code."
E2171193 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: Teresa Colvin | Statement: [The Chicago Code, hasMainCharacter, Teresa Colvin]
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: Teresa Colvin
Triple: [The Chicago Code, hasMainCharacter, Teresa Colvin]
Generated description
Teresa Colvin is the determined and principled Chicago police superintendent who leads the fight against corruption in the television drama "The Chicago Code."

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_69f3492ccc80819086ef7d26e9786647 completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6c6ee1ffc819080cfccc716fe9b94 completed May 3, 2026, 3:54 a.m.
NED1 Entity disambiguation (via context triple) batch_6a390d27217c8190ba7de8cd5b44290f completed June 22, 2026, 10:23 a.m.
NEDg Description generation batch_6a390dd6ff808190bdd0b30b261092f5 completed June 22, 2026, 10:26 a.m.
NED2 Entity disambiguation (via description) batch_6a390f2d062481908c4789fba5096e69 completed June 22, 2026, 10:32 a.m.
Created at: May 1, 2026, 1:06 a.m.