Triple
T27034025
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miss Universe 1987 |
E681002
|
entity |
| Predicate | firstRunnerUpCountry |
P35410
|
FINISHED |
| Object | United States |
—
|
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: United States | Statement: [Miss Universe 1987, firstRunnerUpCountry, United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstRunnerUpCountry Context triple: [Miss Universe 1987, firstRunnerUpCountry, United States]
-
A.
runnerUpCountry
chosen
Indicates the country that finished in second place in a competition or ranking.
-
B.
secondMedalCountry
Indicates the country that received the second-place medal in a given event or competition.
-
C.
secondRunnerUp
Indicates that one entity finished in third place in a competition or ranking relative to the others.
-
D.
countryOfRunnerUpClub
Indicates the country to which the club that finished as runner-up in a competition belongs.
-
E.
secondRoundRunnerUp
Indicates that an entity finished in third place (runner-up to the runner-up) in the second round of a competition or selection process.
- 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_69fedd5a5f4c8190acce88db56303703 |
completed | May 9, 2026, 7:08 a.m. |
| PD | Predicate disambiguation | batch_69fed910b31c8190ae837163d146738d |
completed | May 9, 2026, 6:49 a.m. |
Created at: April 27, 2026, 7:15 a.m.