Triple
T22837896
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Díaz |
E565994
|
entity |
| Predicate | hasFrequencyRankInLatinAmerica |
P97328
|
FINISHED |
| Object | high |
—
|
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: high | Statement: [Díaz, hasFrequencyRankInLatinAmerica, high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFrequencyRankInLatinAmerica Context triple: [Díaz, hasFrequencyRankInLatinAmerica, high]
-
A.
frequencyInLatinAmerica
chosen
Indicates how often something occurs or is present within Latin America.
-
B.
hasPassengerTrafficRankInLatinAmerica
Indicates the relative position of an entity in terms of passenger traffic volume compared to other entities within Latin America.
-
C.
populationRankInMexico
Indicates the relative position of an entity in terms of population size compared to other entities within Mexico.
-
D.
chartPositionARIALatinAmerica
Indicates the chart ranking or position of the song "ARIA" within music charts specific to the Latin America region.
-
E.
selectionRankingFrequency
Indicates how often an entity is chosen or ranked in a particular position within a selection or ordering 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_69e245869e188190a196584f36e682da |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17e8244dc819089c0a7525fb512ab |
completed | April 29, 2026, 3:44 a.m. |
| PD | Predicate disambiguation | batch_69eed2d117088190acbfe130d84f8627 |
completed | April 27, 2026, 3:06 a.m. |
Created at: April 17, 2026, 3:35 p.m.