Triple
T26759378
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Roja |
E674757
|
entity |
| Predicate | CopaAmerica2015HostCountry |
P78048
|
FINISHED |
| Object | Chile |
—
|
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: Chile | Statement: [La Roja, CopaAmerica2015HostCountry, Chile]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: CopaAmerica2015HostCountry Context triple: [La Roja, CopaAmerica2015HostCountry, Chile]
-
A.
copaAmerica2015Host
chosen
Indicates that the subject is the country or entity that hosted the 2015 Copa América tournament.
-
B.
copaAmerica2015FinalResult
Indicates the outcome or final score of the 2015 Copa América tournament’s championship match.
-
C.
hostCountry2015
Indicates the country that served as the official host in the year 2015 for the relevant event or activity.
-
D.
wonRecopaSudamericana
Indicates that one entity has won the Recopa Sudamericana football competition.
-
E.
copaAmericaRunnerUp
Indicates that the subject finished as the second-place team (runner-up) in a specified edition of the Copa América tournament.
- 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_69eecda6e9dc81908452fab3ba17ed9b |
completed | April 27, 2026, 2:44 a.m. |
| NER | Named-entity recognition | batch_69f618dafb6c8190b4f53a7fcbf967e3 |
completed | May 2, 2026, 3:31 p.m. |
| PD | Predicate disambiguation | batch_69f60b8dfa0c8190864e1a940024d0a0 |
completed | May 2, 2026, 2:34 p.m. |
Created at: April 27, 2026, 3:57 a.m.