Triple
T37963353
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Esporte Clube Novo Hamburgo |
E947065
|
entity |
| Predicate | campeonatoGauchoTitle |
P189673
|
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: [Esporte Clube Novo Hamburgo, campeonatoGauchoTitle, 2017]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: campeonatoGauchoTitle Context triple: [Esporte Clube Novo Hamburgo, campeonatoGauchoTitle, 2017]
-
A.
copaDoBrasilTitleYear
Indicates the specific year in which a team won the Copa do Brasil title.
-
B.
copaDoBrasilTitles
Indicates the number of Copa do Brasil titles that an entity (typically a football club) has won.
-
C.
campeonatoBrasileiroSerieATitleYear
Indicates the specific year in which an entity won the Campeonato Brasileiro Série A title.
-
D.
wonSupercopaSudamericana
Indicates that one entity has won the Supercopa Sudamericana football competition.
-
E.
campeonatoBrasileiroSerieATitles
Indicates the number of Campeonato Brasileiro Série A championship titles an entity has won.
- 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_69f76ef7062c819091bfacb7e83aa1e0 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fbc7b78f9481909f4f8fc2e3fdcde1 |
completed | May 6, 2026, 10:59 p.m. |
| PD | Predicate disambiguation | batch_69fbbd18c9908190928d274f8731dfa8 |
completed | May 6, 2026, 10:13 p.m. |
| PDg | Predicate description generation | batch_69fbc7b6c2c88190ad4f58980834053c |
completed | May 6, 2026, 10:59 p.m. |
Created at: May 3, 2026, 4:20 p.m.