Triple
T19531666
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Richard W. Hamming |
E488670
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Hamming |
—
|
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: Hamming | Statement: [Richard W. Hamming, familyName, Hamming]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hamming Context triple: [Richard W. Hamming, familyName, Hamming]
-
A.
Hamming
chosen
Hamming is a surname most notably associated with Richard W. Hamming, an American mathematician and computer scientist known for his pioneering work in error-correcting codes and numerical methods.
-
B.
Hamming code
Hamming code is a family of error-detecting and error-correcting binary codes that enable the automatic detection and correction of single-bit errors in transmitted or stored data.
-
C.
Ham
Ham is a municipality in the Belgian province of Limburg, known for its rural character and location in the Flemish Region.
-
D.
Ham
Ham is a suburban riverside district in southwest London, England, known for its historic houses, green spaces, and proximity to the River Thames.
-
E.
Ham
Ham is a small town in the Somme department of northern France, known historically for its medieval fortress and strategic location.
- 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_69d8e8db5b6c8190984b61f91981f575 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6363fd1f8819080805346efad2579 |
completed | April 20, 2026, 2:20 p.m. |
Created at: April 10, 2026, 1:41 p.m.