Triple
T19075184
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SEGIB |
E466884
|
entity |
| Predicate | focusesOnCountries |
P9827
|
FINISHED |
| Object | Spanish-speaking countries |
—
|
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: Spanish-speaking countries | Statement: [SEGIB, focusesOnCountries, Spanish-speaking countries]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: focusesOnCountries Context triple: [SEGIB, focusesOnCountries, Spanish-speaking countries]
-
A.
seeCountry
Indicates that one entity observes, visits, or becomes aware of a particular country.
-
B.
rangeCountries
Indicates the set of countries over which something (such as a service, product, or data coverage) is available, applicable, or valid.
-
C.
countryTargeted
chosen
Indicates that a particular country is the intended object or focus of an action, operation, or influence.
-
D.
coveredCountry
Indicates that one entity includes or provides coverage for the territory or jurisdiction of a specified country.
-
E.
studCountry
Indicates that a student is studying in, or is associated with studies in, a particular country.
- 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_69d8dd04f4488190b1121cc53ef2bfd6 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5e2e3c7b08190bf6448ead11ba916 |
completed | April 20, 2026, 8:25 a.m. |
| PD | Predicate disambiguation | batch_69e4b99f602881909eeb9c780597e0e6 |
completed | April 19, 2026, 11:16 a.m. |
Created at: April 10, 2026, 12:04 p.m.