Triple
T27034029
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miss Universe 1987 |
E681002
|
entity |
| Predicate | thirdRunnerUpCountry |
P99089
|
FINISHED |
| Object | Venezuela |
—
|
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: Venezuela | Statement: [Miss Universe 1987, thirdRunnerUpCountry, Venezuela]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: thirdRunnerUpCountry Context triple: [Miss Universe 1987, thirdRunnerUpCountry, Venezuela]
-
A.
thirdPlaceCountry
Indicates the country that finished in third place in a given competition or ranking.
-
B.
thirdWinnerCountry
chosen
Indicates the country associated with the entity that placed third in a competition or ranking.
-
C.
thirdPlaceCountryCode
Indicates the country (by its code) that finished in third place in a given competition or ranking.
-
D.
fourthRunnerUpCountry
Indicates the country that finished in fourth place (as the fourth runner-up) in a given competition or ranking.
-
E.
countryOfRunnerUpClub
Indicates the country to which the club that finished as runner-up in a competition belongs.
- 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_69eeeb5566f08190813daf896fa3da04 |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69f6a8df16a88190a23820e64a3b1f92 |
completed | May 3, 2026, 1:46 a.m. |
| PD | Predicate disambiguation | batch_69f6a751d5e48190a77dcecbe7ef9f0b |
completed | May 3, 2026, 1:39 a.m. |
Created at: April 27, 2026, 7:15 a.m.