Triple
T10702045
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tua Tagovailoa |
E252300
|
entity |
| Predicate | fullName |
P16
|
FINISHED |
| Object | Tuanigamanuolepola Tagovailoa |
E252300
|
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: Tuanigamanuolepola Tagovailoa | Statement: [Tua Tagovailoa, fullName, Tuanigamanuolepola Tagovailoa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tuanigamanuolepola Tagovailoa Context triple: [Tua Tagovailoa, fullName, Tuanigamanuolepola Tagovailoa]
-
A.
Tua Tagovailoa
chosen
Tua Tagovailoa is an NFL quarterback for the Miami Dolphins known for his accuracy, quick release, and standout collegiate career at the University of Alabama.
-
B.
Amouli
Amouli is a coastal village located on the island of Tutuila in American Samoa.
-
C.
Sione Tuita
Sione Tuita is a Tongan political figure who has served as Prime Minister of the Kingdom of Tonga.
-
D.
Sione Tupou Mateialona
Sione Tupou Mateialona was a Tongan political leader who served as the country’s prime minister.
-
E.
Sione
Sione is a fictional character played by New Zealand actor Julian Dennison, best known from his comedic and coming-of-age film roles.
- 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_69d6aa5cbabc8190973e683950d89faf |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6fd8c835c8190bf1a67ee94195926 |
completed | April 9, 2026, 1:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d998f5cda081909932daa3c98f8b46 |
completed | April 11, 2026, 12:42 a.m. |
Created at: April 8, 2026, 9:12 p.m.