Triple
T18642102
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Biles (vault, Yurchenko half-on front layout half-off) |
E455713
|
entity |
| Predicate | onTableTurn |
P28004
|
FINISHED |
| Object | half turn (½ turn) onto the table |
—
|
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: half turn (½ turn) onto the table | Statement: [Biles (vault, Yurchenko half-on front layout half-off), onTableTurn, half turn (½ turn) onto the table]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: onTableTurn Context triple: [Biles (vault, Yurchenko half-on front layout half-off), onTableTurn, half turn (½ turn) onto the table]
-
A.
turns
chosen
Indicates a change in orientation, direction, or state initiated by one entity affecting itself or another entity.
-
B.
turnsIn
Indicates that an entity submits or hands over something, typically work or an item, to another party or authority.
-
C.
turnout
Indicates the number or proportion of participants who attend or take part in an event or activity.
-
D.
turnedPro
Indicates that an individual transitioned from amateur status to professional status in a particular field or activity.
-
E.
tookPositionOn
Indicates that an entity expressed or adopted a specific stance, opinion, or viewpoint regarding a particular issue, topic, or subject.
- F. None of above.
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_69d8d38ea1e88190997e9b231190ba6f |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e54fcd6da081908030b052727f2c2f |
completed | April 19, 2026, 9:57 p.m. |
| PD | Predicate disambiguation | batch_69e478d85864819093cbad5ed9b54878 |
completed | April 19, 2026, 6:40 a.m. |
Created at: April 10, 2026, 11:47 a.m.