Triple
T18222174
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | tidyr |
E436332
|
entity |
| Predicate | developer |
P73
|
FINISHED |
| Object | Hadley Wickham |
—
|
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: Hadley Wickham | Statement: [tidyr, developer, Hadley Wickham]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hadley Wickham Context triple: [tidyr, developer, Hadley Wickham]
-
A.
Hadley Wickham
chosen
Hadley Wickham is a prominent statistician and software developer best known for creating many of the core R packages in the tidyverse, which have transformed data analysis and visualization in R.
-
B.
Ross Ihaka
Ross Ihaka is a New Zealand statistician best known as one of the original creators of the R programming language.
-
C.
Martin Maechler
Martin Maechler is a Swiss statistician and software developer known for his significant contributions to the R programming language and statistical computing.
-
D.
Douglas Bates
Douglas Bates is a statistician and R developer best known for his work on mixed-effects models and for co-authoring the lme4 package.
-
E.
Ben Bolker
Ben Bolker is a statistician and ecologist known for his contributions to mixed-effects modeling and statistical software in R.
- 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_69e4e47c85108190bd9707b40bdfdb38 |
completed | April 19, 2026, 2:19 p.m. |
Created at: April 10, 2026, 10:32 a.m.