Triple

T25526232
Position Surface form Disambiguated ID Type / Status
Subject Marshall Kirk McKusick E639782 entity
Predicate coAuthor P398 FINISHED
Object Samuel J. Leffler
Samuel J. Leffler is a computer scientist and software engineer best known for his significant contributions to the BSD Unix operating system and related networking and filesystem technologies.
E1683424 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: Samuel J. Leffler | Statement: [Marshall Kirk McKusick, coAuthor, Samuel J. Leffler]
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: Samuel J. Leffler
Triple: [Marshall Kirk McKusick, coAuthor, Samuel J. Leffler]
Generated description
Samuel J. Leffler is a computer scientist and software engineer best known for his significant contributions to the BSD Unix operating system and related networking and filesystem technologies.

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_69e75dbf3f9c8190b3f2a75d1b75d127 completed April 21, 2026, 11:21 a.m.
NER Named-entity recognition batch_69f5f85fabdc8190aa4cab88fd743335 completed May 2, 2026, 1:13 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10ad88cd5c819093c3b14361806d56 completed May 22, 2026, 7:24 p.m.
NEDg Description generation batch_6a10ae583b108190801bf4219bf2467a completed May 22, 2026, 7:28 p.m.
NED2 Entity disambiguation (via description) batch_6a10af719e6c8190bbd23598b3426106 completed May 22, 2026, 7:33 p.m.
Created at: April 21, 2026, 3:10 p.m.