Triple
T20125246
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr. Krupp |
E490732
|
entity |
| Predicate | fullName |
P16
|
FINISHED |
| Object | Mr. Benjamin Krupp |
—
|
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: Mr. Benjamin Krupp | Statement: [Mr. Krupp, fullName, Mr. Benjamin Krupp]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mr. Benjamin Krupp Context triple: [Mr. Krupp, fullName, Mr. Benjamin Krupp]
-
A.
Benjamin Krupp
chosen
Benjamin Krupp is the grumpy elementary school principal who transforms into the goofy superhero Captain Underpants in Dav Pilkey’s children’s book series.
-
B.
Fred Krupp
Fred Krupp is an American environmental leader and longtime president of the Environmental Defense Fund, known for his work on market-based solutions to climate change and pollution.
-
C.
Karl Krueger
Karl Krueger was an American conductor known for his work with major orchestras and for making early high-fidelity orchestral recordings.
-
D.
William Wiegand
William Wiegand was an American writer and critic known for his contributions to mid-20th-century literary culture.
-
E.
Carl Spackler
Carl Spackler is the eccentric, gopher-obsessed groundskeeper played by Bill Murray in the golf comedy film "Caddyshack."
- 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_69da62651a0c8190a3e05e95e056a66b |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e667412b888190b43f7dd1ccdbad01 |
completed | April 20, 2026, 5:49 p.m. |
Created at: April 11, 2026, 11:31 p.m.