Triple

T18659837
Position Surface form Disambiguated ID Type / Status
Subject Frank Butler E456160 entity
Predicate alsoKnownAs P39 FINISHED
Object Frank Russell Butler
Frank Russell Butler was an American screenwriter and actor active in early 20th-century Hollywood, known for his work on films such as "Going My Way."
 E1798478 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: Frank Russell Butler | Statement: [Frank Butler, alsoKnownAs, Frank Russell Butler]
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: Frank Russell Butler
Triple: [Frank Butler, alsoKnownAs, Frank Russell Butler]
Generated description
Frank Russell Butler was an American screenwriter and actor active in early 20th-century Hollywood, known for his work on films such as "Going My Way."

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_69d8d38f72b4819090a935175d9ca8af completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e550889580819090ceb0ea4c93acc7 completed April 19, 2026, 10 p.m.
NED1 Entity disambiguation (via context triple) batch_6a15b85ef98c8190baad6ec7a882fabd completed May 26, 2026, 3:12 p.m.
NEDg Description generation batch_6a15b8dc4ad48190a8a155c34409a6e0 completed May 26, 2026, 3:14 p.m.
NED2 Entity disambiguation (via description) batch_6a15b9fb29a08190854c7d4c69b7fac9 completed May 26, 2026, 3:19 p.m.
Created at: April 10, 2026, 11:48 a.m.