Triple
T961196
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lamar Hunt |
E20739
|
entity |
| Predicate | givenName |
P17
|
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: [Lamar Hunt, givenName, Lamar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lamar Context triple: [Lamar Hunt, givenName, Lamar]
-
A.
Lamar
chosen
Lamar is a surname most notably associated with Mirabeau B. Lamar, the second president of the Republic of Texas.
-
B.
Winfield
Winfield is a masculine given name most notably borne by 19th-century American military leader Winfield Scott.
-
C.
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.
-
D.
Bakke
Bakke is the commonly used shorthand name for the landmark U.S. Supreme Court case Regents of the University of California v. Bakke, which addressed the constitutionality of race-based admissions policies in higher education.
-
E.
Lamon
Lamon is an archaeological site notable for inscriptions in the ancient Venetic language.
- 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_69a493b21f2881908132dcf45dcd2f36 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b4144c208190980936347a95e233 |
completed | March 1, 2026, 9:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac11a3f8c481908f9ed37c44788cb7 |
completed | March 7, 2026, 11:53 a.m. |
Created at: March 1, 2026, 7:40 p.m.