Triple
T2080639
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joseph Rucker Lamar |
E45230
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Lamar |
E45230
|
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: Lamar | Statement: [Joseph Rucker Lamar, familyName, Lamar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lamar Context triple: [Joseph Rucker Lamar, familyName, Lamar]
-
A.
Lamar
chosen
Lamar is a surname most notably associated with Mirabeau B. Lamar, the second president of the Republic of Texas.
-
B.
Lamar Trotti
Lamar Trotti was an American screenwriter and producer best known for his work on classic Hollywood films of the 1930s and 1940s, including several major 20th Century Fox productions.
-
C.
Winfield
Winfield is a masculine given name most notably borne by 19th-century American military leader Winfield Scott.
-
D.
Landry
Landry is a surname most famously associated with Tom Landry, the legendary longtime head coach of the Dallas Cowboys in the National Football League.
-
E.
Katy
Katy is the popular nickname for the Missouri–Kansas–Texas Railroad, a historic American railway that served the central and southern United States.
- 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_69a8891869c88190a02643e3bb746f59 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abba345be48190a1895f388e7749e5 |
completed | March 7, 2026, 5:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae518752148190bd7524872d70da7e |
completed | March 9, 2026, 4:50 a.m. |
Created at: March 4, 2026, 7:41 p.m.