Triple
T9634379
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Definition of Standard ML |
E232891
|
entity |
| Predicate | author |
P4
|
FINISHED |
| Object | Mads Tofte |
E808840
|
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: Mads Tofte | Statement: [The Definition of Standard ML, author, Mads Tofte]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mads Tofte Context triple: [The Definition of Standard ML, author, Mads Tofte]
-
A.
Mads Tofte
chosen
Mads Tofte is a Danish computer scientist known for his influential work on the design and implementation of the Standard ML programming language and its type system.
-
B.
Søren Stærmose
Søren Stærmose is a Danish film producer best known for his work on adaptations of Stieg Larsson’s Millennium series, including The Girl with the Dragon Tattoo.
-
C.
Jesper Nøhr
Jesper Nøhr is a Danish software developer and entrepreneur best known for creating the code hosting platform Bitbucket.
-
D.
Morten Rasmussen
Morten Rasmussen is a Danish former professional footballer known for his prolific goal-scoring as a striker, particularly in the Danish Superliga.
-
E.
Jens Toldstrup
Jens Toldstrup was a prominent Danish resistance leader during World War II, known for organizing sabotage and intelligence operations against the German occupation.
- 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_69ca848940cc8190b97cec654cb3bb4a |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9b2a0e2c8190ab5aaa223b1e1cde |
completed | April 1, 2026, 10:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d18237e2608190a3e7d45231a35efd |
completed | April 4, 2026, 9:27 p.m. |
Created at: March 30, 2026, 8:11 p.m.