Triple
T18234547
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Well-Grounded Rubyist |
E436637
|
entity |
| Predicate | author |
P4
|
FINISHED |
| Object | David A. Black |
—
|
NE NERFINISHED |
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: David A. Black | Statement: [The Well-Grounded Rubyist, author, David A. Black]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: David A. Black Context triple: [The Well-Grounded Rubyist, author, David A. Black]
-
A.
David A. Black
chosen
David A. Black is a Ruby programmer, author, and educator best known for his influential books and contributions to the Ruby community.
-
B.
David L. Black
David L. Black is a computer scientist and networking expert known for his contributions to Internet standards, including work on early congestion control mechanisms in TCP/IP.
-
C.
Paul M. Black
Paul M. Black is an American healthcare technology executive best known for serving as CEO of the electronic health records company Allscripts.
-
D.
Jeremy Black
Jeremy Black is a British historian renowned for his prolific scholarship on military history, international relations, and the history of warfare.
-
E.
David Blackman
David Blackman is a film and television producer known for his executive production work on music-related documentaries and projects.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9103a8081908bbb0836fef10efd |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4f4b5ce608190b6fba518256607da |
completed | April 19, 2026, 3:28 p.m. |
Created at: April 10, 2026, 10:33 a.m.