Triple
T32486485
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gustav Thöni |
E830255
|
entity |
| Predicate | wonWorldChampionshipGoldAt |
P104075
|
FINISHED |
| Object | 1972 Sapporo (World Championships combined event) |
—
|
NE NERFINISHED |
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: 1972 Sapporo (World Championships combined event) | Statement: [Gustav Thöni, wonWorldChampionshipGoldAt, 1972 Sapporo (World Championships combined event)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wonWorldChampionshipGoldAt Context triple: [Gustav Thöni, wonWorldChampionshipGoldAt, 1972 Sapporo (World Championships combined event)]
-
A.
worldChampionshipsGoldMedal
Indicates that the subject has won a gold medal at a world championship competition.
-
B.
hasWorldChampionship
Indicates that an entity possesses, has won, or holds a world championship title or status.
-
C.
worldChampionshipGoldMedals
Indicates the number of gold medals an entity has won at world championship competitions.
-
D.
wonWorldTitleIn
chosen
Indicates that an entity achieved victory in a world championship title in a specified competition or year.
-
E.
wonMedalAt
Indicates that an entity received a medal as a result of participating in a specific event or competition.
- 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_69f34920aa4081908d8fb0277414b911 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6c8159edc8190b1c87015e0c820e8 |
completed | May 3, 2026, 3:59 a.m. |
| PD | Predicate disambiguation | batch_69f6c3f42fbc8190a06eb1044c9e6094 |
completed | May 3, 2026, 3:41 a.m. |
Created at: May 1, 2026, 12:58 a.m.