Triple
T3018744
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bryant Gumbel |
E82401
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Gumbel |
E82401
|
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: Gumbel | Statement: [Bryant Gumbel, familyName, Gumbel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gumbel Context triple: [Bryant Gumbel, familyName, Gumbel]
-
A.
Gumbel
chosen
Gumbel is a surname most notably associated with American sportscaster Greg Gumbel.
-
B.
Bernoulli
Bernoulli is the surname of a prominent Swiss family of mathematicians and scientists, including figures such as Jakob, Johann, and Daniel Bernoulli, who made foundational contributions to calculus, probability, and fluid dynamics.
-
C.
Parzen
Parzen is a surname most notably associated with Emanuel Parzen, an American statistician known for the Parzen window method in probability and statistics.
-
D.
Gibbs
Gibbs is a common English surname borne by various notable individuals across fields such as sports, science, and entertainment.
-
E.
Cauchy distribution
The Cauchy distribution is a continuous probability distribution with heavy tails and undefined mean and variance, often used as a classic example of pathological behavior in probability theory and statistics.
- 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_69ad8b1fb34081908c1b873e2b7273e1 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9a927b608190ba1392498507b237 |
completed | March 8, 2026, 3:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1dea9a7c4819087fb6853d839fb1e |
completed | March 11, 2026, 9:29 p.m. |
Created at: March 8, 2026, 3 p.m.