Triple
T10702069
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tua Tagovailoa |
E252300
|
entity |
| Predicate | madeCollegiateDebut |
P95355
|
FINISHED |
| Object | 2017 |
—
|
LITERAL 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: 2017 | Statement: [Tua Tagovailoa, madeCollegiateDebut, 2017]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: madeCollegiateDebut Context triple: [Tua Tagovailoa, madeCollegiateDebut, 2017]
-
A.
playedForCollegeTeamFrom
Indicates that an individual was a member of and played for a specific college team starting in a given year.
-
B.
playedCollegeSport
Indicates that the subject participated in an organized college-level sport for the object institution.
-
C.
playedCollegeYears
Indicates the span of years during which an entity participated in college-level play (e.g., as a student-athlete).
-
D.
collegeChampionshipYear
Indicates the specific year in which a college team or individual won a championship title.
-
E.
positionPlayedInCollege
Indicates the specific playing position an individual held on a sports team during their college career.
- F. None of above. chosen
Provenance (4 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. |
| PD | Predicate disambiguation | batch_69d6dd8ea6408190a800f9cb57372189 |
completed | April 8, 2026, 10:58 p.m. |
| PDg | Predicate description generation | batch_69d6df47899481909ac0e518d94883cb |
completed | April 8, 2026, 11:05 p.m. |
Created at: April 8, 2026, 9:12 p.m.