Triple
T9117099
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lamar Trotti |
E218747
|
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 Trotti, givenName, Lamar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lamar Context triple: [Lamar Trotti, 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.
Gatlin
Gatlin is a surname of English origin borne by various notable individuals across fields such as music, sports, and politics.
-
C.
Parmer
Parmer is a surname and place name that serves as a variant spelling of Palmer.
-
D.
Brantley
Brantley is a small town located in Crenshaw County in the state of Alabama, United States.
-
E.
Yarborough
Yarborough is an English surname of likely toponymic origin, historically associated with various notable families in Britain.
- 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_69ca83dc94ac8190b9ef42684d36ff39 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca8a4c9e08190ba3603a5d00afb20 |
completed | April 1, 2026, 5:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0307299ec8190acade4f388642e23 |
completed | April 3, 2026, 9:26 p.m. |
Created at: March 30, 2026, 7:17 p.m.