Triple
T5783531
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miranda Lambert |
E128216
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Lambert |
E255544
|
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: Lambert | Statement: [Miranda Lambert, familyName, Lambert]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lambert Context triple: [Miranda Lambert, familyName, Lambert]
-
A.
Lambert
chosen
Lambert is a masculine given name of Germanic origin, historically borne by various saints, nobles, and notable figures in Europe.
-
B.
Nantz
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.
-
C.
Lampert
Lampert is a surname most notably associated with American billionaire investor and former Sears Holdings CEO Edward Lampert.
-
D.
Lamont
Lamont is an unincorporated community in Kern County, California, known primarily as an agricultural and residential area near Bakersfield.
-
E.
Lamont
Lamont is a surname of Scottish origin borne by various notable individuals in politics, finance, academia, and the arts.
- 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_69c0084450048190bc647b649a05136b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c02a184870819084251554eae1e33c |
completed | March 22, 2026, 5:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c098112a1c8190a8aacb208c544959 |
completed | March 23, 2026, 1:32 a.m. |
Created at: March 22, 2026, 3:50 p.m.