Triple
T2858651
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lucius Quintus Cincinnatus Lamar |
E63263
|
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: [Lucius Quintus Cincinnatus Lamar, familyName, Lamar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lamar Context triple: [Lucius Quintus Cincinnatus 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.
Parmer
Parmer is a surname and place name that serves as a variant spelling of Palmer.
-
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_69ab4c41e8c08190a9e8f5249cc12610 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abdf89d3b4819086936f26d8683a2a |
completed | March 7, 2026, 8:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b01d91f6448190866f6ee0491041cf |
completed | March 10, 2026, 1:33 p.m. |
Created at: March 6, 2026, 10:02 p.m.