Triple

T27033547
Position Surface form Disambiguated ID Type / Status
Subject Friday Night Lights (season 4) E680990 entity
Predicate featuresCharacter P626 FINISHED
Object Luke Cafferty
Luke Cafferty is a high school football player in the television series "Friday Night Lights," known for his talent on the field and the personal and moral challenges he faces off it.
E1783014 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: Luke Cafferty | Statement: [Friday Night Lights (season 4), featuresCharacter, Luke Cafferty]
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: Luke Cafferty
Triple: [Friday Night Lights (season 4), featuresCharacter, Luke Cafferty]
Generated description
Luke Cafferty is a high school football player in the television series "Friday Night Lights," known for his talent on the field and the personal and moral challenges he faces off it.

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_69eeeb5566f08190813daf896fa3da04 completed April 27, 2026, 4:51 a.m.
NER Named-entity recognition batch_69f6223870308190a016b76902bcf6d4 completed May 2, 2026, 4:11 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12da657a708190bba997c6be72b4e4 completed May 24, 2026, 11 a.m.
NEDg Description generation batch_6a12db5d2878819094252a665596a86e completed May 24, 2026, 11:05 a.m.
NED2 Entity disambiguation (via description) batch_6a12dbe4c9e4819084be4a4f5e3b58a6 completed May 24, 2026, 11:07 a.m.
Created at: April 27, 2026, 7:15 a.m.