Triple
T377051
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1984 Summer Olympics |
E8393
|
entity |
| Predicate | MaryLouRettonAchievement |
P10448
|
FINISHED |
| Object | first American woman to win Olympic all-around gymnastics gold |
—
|
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: first American woman to win Olympic all-around gymnastics gold | Statement: [1984 Summer Olympics, MaryLouRettonAchievement, first American woman to win Olympic all-around gymnastics gold]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: MaryLouRettonAchievement Context triple: [1984 Summer Olympics, MaryLouRettonAchievement, first American woman to win Olympic all-around gymnastics gold]
-
A.
formerWorldNo1
Indicates that the subject was ranked number one in the world in the past, but does not hold that top ranking currently.
-
B.
majorChampionshipWins
Indicates the number of major championship titles an entity has won.
-
C.
grandSlamDoublesTitles
Indicates the number of Grand Slam tennis doubles titles an entity has won.
-
D.
grandSlamSinglesTitles
Indicates the number of Grand Slam singles tennis titles an entity has won.
-
E.
careerWins
Indicates the total number of wins an individual or entity has accumulated over the course of their entire 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_69a2e7f2ec648190b42bc7db424f8109 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ec1804108190a1e94526b71289ea |
completed | Feb. 28, 2026, 1:22 p.m. |
| PD | Predicate disambiguation | batch_69a2e96351cc8190a55adf95f8c27e9e |
completed | Feb. 28, 2026, 1:10 p.m. |
| PDg | Predicate description generation | batch_69a2ea0be90881909e32d8fdb7aabb29 |
completed | Feb. 28, 2026, 1:13 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.