Triple
T4539873
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stephen P. Boyd |
E107500
|
entity |
| Predicate | hasWrittenTextbookOn |
P14097
|
FINISHED |
| Object | convex optimization |
—
|
LITERAL 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: convex optimization | Statement: [Stephen P. Boyd, hasWrittenTextbookOn, convex optimization]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWrittenTextbookOn Context triple: [Stephen P. Boyd, hasWrittenTextbookOn, convex optimization]
-
A.
hasWrittenAbout
chosen
Indicates that one entity has authored content or material discussing, analyzing, or referencing another entity.
-
B.
hasWrittenWorkType
Indicates that an entity (typically a written work) is associated with a specific type or category of written work (such as novel, article, report, etc.).
-
C.
areWrittenOn
Indicates that one entity serves as a surface or medium on which another entity is inscribed, recorded, or written.
-
D.
hasWrittenFor
Indicates that one entity has created written content (such as articles, stories, or texts) for or on behalf of another entity, typically a publication, organization, or platform.
-
E.
notableWorkWrittenThere
Indicates that a notable work was written at or in the specified place.
- F. None of above.
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_69bd43f922788190b7edfa294e39b178 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd57bb5c0c819092ebb2dd3310f5f8 |
completed | March 20, 2026, 2:20 p.m. |
| PD | Predicate disambiguation | batch_69bd5220e40481908ca2d7e2c43d8531 |
completed | March 20, 2026, 1:56 p.m. |
Created at: March 20, 2026, 1:04 p.m.