Triple

T32173257
Position Surface form Disambiguated ID Type / Status
Subject Maniac Cop E821766 entity
Predicate mainCharacter P1183 FINISHED
Object Teresa Mallory
Teresa Mallory is a central character in the Maniac Cop film series, portrayed as a determined and resourceful woman entangled in the investigation of a murderous, seemingly unstoppable police officer.
 E1998928 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 Mallory | Statement: [Maniac Cop, mainCharacter, Teresa Mallory]
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 Mallory
Triple: [Maniac Cop, mainCharacter, Teresa Mallory]
Generated description
Teresa Mallory is a central character in the Maniac Cop film series, portrayed as a determined and resourceful woman entangled in the investigation of a murderous, seemingly unstoppable police officer.

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_69f3490699a48190bbef96b198e8fade completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6ba77b7288190a0f2b12c5df8ee3e completed May 3, 2026, 3:01 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2f46c0871881908373cb7794f55604 completed June 15, 2026, 12:26 a.m.
NEDg Description generation batch_6a2f4759204c8190903a9b022f3e4816 completed June 15, 2026, 12:29 a.m.
NED2 Entity disambiguation (via description) batch_6a2f4841adc08190bb640f6efb2359a0 completed June 15, 2026, 12:33 a.m.
Created at: May 1, 2026, 12:33 a.m.