Triple
T15799972
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apprenticeship Patterns |
E383072
|
entity |
| Predicate | author |
P4
|
FINISHED |
| Object | Dave Hoover |
E80881
|
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: Dave Hoover | Statement: [Apprenticeship Patterns, author, Dave Hoover]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dave Hoover Context triple: [Apprenticeship Patterns, author, Dave Hoover]
-
A.
Dave Hoover
chosen
Dave Hoover is a software developer and author known for his work on agile practices and the book "Apprenticeship Patterns."
-
B.
Ben Brockman
Ben Brockman is a mischievous and quick-witted middle child in the British sitcom "Outnumbered," known for his imaginative questions and offbeat logic.
-
C.
Jon Oberheide
Jon Oberheide is a cybersecurity entrepreneur and researcher best known as the co-founder and former CTO of Duo Security, a leading multi-factor authentication and zero-trust security company.
-
D.
Steven Bethard
Steven Bethard is a computer scientist and natural language processing researcher known for his work on temporal information extraction, semantic role labeling, and clinical NLP.
-
E.
Alex Heineman
Alex Heineman is a film producer known for his work on the historical thriller "Operation Finale" and other feature films.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d86da16e188190b89af699f1ed0bfe |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e0b4e135b08190b736e77bac5e2bff |
completed | April 16, 2026, 10:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff90b08ab48190892c700f5eb261d8 |
completed | May 9, 2026, 7:53 p.m. |
Created at: April 10, 2026, 4:48 a.m.