Triple
T22608758
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Data Mining: Concepts and Techniques |
E566635
|
entity |
| Predicate | author |
P4
|
FINISHED |
| Object | Micheline Kamber |
—
|
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: Micheline Kamber | Statement: [Data Mining: Concepts and Techniques, author, Micheline Kamber]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Micheline Kamber Context triple: [Data Mining: Concepts and Techniques, author, Micheline Kamber]
-
A.
Micheline Kamber
chosen
Micheline Kamber is a computer scientist and co-author of the influential data mining textbook "Data Mining: Concepts and Techniques."
-
B.
Micheline Winter
Micheline Winter was the wife of renowned French filmmaker and actor Jacques Tati.
-
C.
Micheline Ostermeyer
Micheline Ostermeyer was a French athlete and concert pianist renowned for winning multiple track and field medals at the 1948 Olympic Games.
-
D.
Micheline Borgogno
Micheline Borgogno is a French local politician serving as the mayor of the commune of Montsoult in northern France.
-
E.
Marceline Loridan-Ivens
Marceline Loridan-Ivens was a French filmmaker, writer, and Holocaust survivor known for her documentary work and autobiographical reflections on memory and exile.
- 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_69e245884860819081046ce07d5872c4 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f167e86794819097e9c1ea83db52e6 |
completed | April 29, 2026, 2:07 a.m. |
Created at: April 17, 2026, 2:55 p.m.