Triple
T11306809
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jim Nantz |
E267734
|
entity |
| Predicate | hasFamilyName |
P18
|
FINISHED |
| Object | Nantz |
E267734
|
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: Nantz | Statement: [Jim Nantz, hasFamilyName, Nantz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nantz Context triple: [Jim Nantz, hasFamilyName, Nantz]
-
A.
Nantz
chosen
Nantz is the surname of Jim Nantz, a prominent American sportscaster best known for his long-running work with CBS Sports covering events like the NFL, NCAA basketball, and The Masters.
-
B.
Sauvy
Sauvy is a French surname most notably borne by Alfred Sauvy, a prominent demographer, sociologist, and economist.
-
C.
Taconnaz
Taconnaz is a locality in the Chamonix valley of the French Alps, known for giving its name to the nearby Glacier de Taconnaz.
-
D.
Doncieux
Doncieux is a French surname most notably associated with Camille Doncieux, the first wife and frequent model of painter Claude Monet.
-
E.
Choully
Choully is a small wine-producing village in the commune of Satigny in the canton of Geneva, Switzerland.
- 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_69d6aaca5c24819083db46a30d86cb34 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e9bf87d88190904c2d174578ebbf |
completed | April 9, 2026, 6:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e525b4bdb88190b22d64eb65e97d9d |
completed | April 19, 2026, 6:57 p.m. |
Created at: April 8, 2026, 9:32 p.m.