Triple
T35753391
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coppa Acerbo |
E1033374
|
entity |
| Predicate | worldChampionshipRoundYear |
P197576
|
FINISHED |
| Object | 1957 |
—
|
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: 1957 | Statement: [Coppa Acerbo, worldChampionshipRoundYear, 1957]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: worldChampionshipRoundYear Context triple: [Coppa Acerbo, worldChampionshipRoundYear, 1957]
-
A.
worldChampionshipMatchYear
Indicates the year in which a given world championship match took place.
-
B.
worldChampionshipTitleYear
Indicates the specific year in which an entity won a world championship title.
-
C.
lastWorldChampionshipEditionYear
Indicates the calendar year in which the most recent edition of a given world championship event took place.
-
D.
worldChampionshipMedalYear
Indicates the year in which an entity received a medal at a world championship event.
-
E.
worldChampionshipBestYear
Indicates the year in which an entity achieved its best or most successful performance in a world championship competition.
- 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_69f76e1262f48190a313318665acc189 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fe9b0276d48190b554fa22b043e6d8 |
completed | May 9, 2026, 2:25 a.m. |
| PD | Predicate disambiguation | batch_69fe999692b081909921e1148d66f0ef |
completed | May 9, 2026, 2:19 a.m. |
| PDg | Predicate description generation | batch_69fe9b013ec481908f89beddb9c4cd4e |
completed | May 9, 2026, 2:25 a.m. |
Created at: May 3, 2026, 4:06 p.m.