Triple

T28783628
Position Surface form Disambiguated ID Type / Status
Subject Talbot School of Theology E726739 entity
Predicate hasNotableFaculty P141 FINISHED
Object Sean McDowell
Sean McDowell is an American Christian apologist, author, and professor known for his work on defending the Christian faith and engaging cultural and ethical issues from an evangelical perspective.
E1860778 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: Sean McDowell | Statement: [Talbot School of Theology, hasNotableFaculty, Sean McDowell]
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: Sean McDowell
Triple: [Talbot School of Theology, hasNotableFaculty, Sean McDowell]
Generated description
Sean McDowell is an American Christian apologist, author, and professor known for his work on defending the Christian faith and engaging cultural and ethical issues from an evangelical perspective.

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_69f0319aabec81908368720196f69a35 completed April 28, 2026, 4:03 a.m.
NER Named-entity recognition batch_69f6584e5b1c8190a83bef97dde015af completed May 2, 2026, 8:02 p.m.
NED1 Entity disambiguation (via context triple) batch_6a25a83769b48190a0b1ed1e080f1cd1 completed June 7, 2026, 5:19 p.m.
NEDg Description generation batch_6a25ac2e9f008190842adcac171d842a completed June 7, 2026, 5:36 p.m.
NED2 Entity disambiguation (via description) batch_6a25b00d0870819080559a7eb818b1ad completed June 7, 2026, 5:53 p.m.
Created at: April 28, 2026, 6:20 a.m.