Triple
T5867358
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DaMarcus Beasley |
E130428
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Beasley |
E130428
|
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: Beasley | Statement: [DaMarcus Beasley, familyName, Beasley]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Beasley Context triple: [DaMarcus Beasley, familyName, Beasley]
-
A.
Beasley
chosen
Beasley is a surname most prominently associated with DaMarcus Beasley, a former United States international soccer player known for his World Cup appearances and club career in MLS and Europe.
-
B.
Beal
Beal is a surname most prominently associated with NBA All-Star shooting guard Bradley Beal.
-
C.
Bealings
Bealings is a small rural village and civil parish located in the English county of Suffolk.
-
D.
Beyton
Beyton is a small rural village and civil parish in the English county of Suffolk, known for its traditional village green and historic buildings.
-
E.
Beaty
Beaty is a surname and given name of English and Irish origin borne by various notable individuals across fields such as politics, sports, 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_69c0085047dc8190af24e311edad3c07 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c035c10648819098c638cdc2084c80 |
completed | March 22, 2026, 6:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0b10fe5908190977d1802258f9f13 |
completed | March 23, 2026, 3:18 a.m. |
Created at: March 22, 2026, 3:56 p.m.