Triple
T9139655
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Circuito Interior |
E219290
|
entity |
| Predicate | officialLanguageArea |
P10102
|
FINISHED |
| Object | Spanish |
—
|
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 | Statement: [Circuito Interior, officialLanguageArea, Spanish]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: officialLanguageArea Context triple: [Circuito Interior, officialLanguageArea, Spanish]
-
A.
languageArea
Indicates the geographic or cultural region in which a particular language is used or predominantly spoken.
-
B.
officialLanguage
Indicates that a particular language has been formally designated by an authority as the official language used for government, legal, or administrative purposes in a given jurisdiction.
-
C.
officialLanguageScope
chosen
Indicates the scope or extent (such as region, institution, or context) within which a language holds official status.
-
D.
regionOfMajorLanguage
Indicates the geographic region where a particular language is predominantly spoken or holds major usage.
-
E.
standardLanguageOf
Indicates that one entity serves as the officially recognized or commonly used standard language for another entity (such as a country, region, or organization).
- 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_69ca83e012288190a5771058adbaabd2 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca8f129c08190b6f053984cb7363f |
completed | April 1, 2026, 5:11 a.m. |
| PD | Predicate disambiguation | batch_69cc6603ce8c8190bf6e8d6754bdec54 |
completed | April 1, 2026, 12:25 a.m. |
Created at: March 30, 2026, 7:19 p.m.