Triple

T27102452
Position Surface form Disambiguated ID Type / Status
Subject Wild America E686477 entity
Predicate mainCharacter P1183 FINISHED
Object Marshall Stouffer
Marshall Stouffer is the adventurous young protagonist of the family film "Wild America," inspired by the real-life wildlife filmmaker brothers who risk danger to capture animals on camera.
E1756132 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: Marshall Stouffer | Statement: [Wild America, mainCharacter, Marshall Stouffer]
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: Marshall Stouffer
Triple: [Wild America, mainCharacter, Marshall Stouffer]
Generated description
Marshall Stouffer is the adventurous young protagonist of the family film "Wild America," inspired by the real-life wildlife filmmaker brothers who risk danger to capture animals on camera.

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_69ef1489f8b481908e24a1985982bd26 completed April 27, 2026, 7:47 a.m.
NER Named-entity recognition batch_69f623b76bec8190ad8f66180c8ecbd4 completed May 2, 2026, 4:17 p.m.
NED1 Entity disambiguation (via context triple) batch_6a124808c1348190803263c4af6e2871 completed May 24, 2026, 12:36 a.m.
NEDg Description generation batch_6a12488822208190aab1355ac3efd2a6 completed May 24, 2026, 12:38 a.m.
NED2 Entity disambiguation (via description) batch_6a124935c01c8190b9d6d13c4f50a104 completed May 24, 2026, 12:41 a.m.
Created at: April 27, 2026, 8:48 a.m.