Triple
T13944480
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Spanish overseas territories |
E335342
|
entity |
| Predicate | languageSpread |
P2266
|
FINISHED |
| Object | Hispanophone world |
—
|
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: Hispanophone world | Statement: [Spanish overseas territories, languageSpread, Hispanophone world]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageSpread Context triple: [Spanish overseas territories, languageSpread, Hispanophone world]
-
A.
languagesSpoken
Indicates that an entity is able to communicate using one or more specified languages.
-
B.
languageGroupSpoken
Indicates that a particular language group is spoken or used for communication by an entity.
-
C.
isWidelySpokenIn
chosen
Indicates that a language is spoken by a large portion of the population across many regions or communities within a specified area.
-
D.
isLinguaFrancaOf
Indicates that a language serves as a common medium of communication between speakers of different native languages within a particular region, community, or context.
-
E.
languageIndependence
Indicates that a concept, method, or representation does not depend on any specific programming or natural language and can be applied uniformly across different languages.
- 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_69d81c6081b88190b53e317c3370c8fe |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2e0f6f3c8190a64058b732b0ac52 |
completed | April 14, 2026, 12:07 p.m. |
| PD | Predicate disambiguation | batch_69de05a3ccf88190b45c742db483fa08 |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 9, 2026, 10:17 p.m.