Triple
T9683631
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monterrey International Airport |
E234348
|
entity |
| Predicate | hasSecondaryLanguageUsage |
P9103
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [Monterrey International Airport, hasSecondaryLanguageUsage, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSecondaryLanguageUsage Context triple: [Monterrey International Airport, hasSecondaryLanguageUsage, English]
-
A.
hasSecondaryLanguage
chosen
Indicates that an entity possesses or uses a secondary language in addition to its primary language.
-
B.
hasSecondaryLanguageTradition
Indicates that an entity possesses an additional, non-primary language tradition associated with it, such as in its use, documentation, or cultural context.
-
C.
hasSecondaryUsage
Indicates that an entity is associated with an additional, non-primary function or purpose beyond its main intended use.
-
D.
laterSecondaryLanguageOfAdministration
Indicates that one language served as a subsequent or later secondary language used for administrative purposes in relation to another language.
-
E.
hasPrimaryLanguage1
Indicates that an entity’s main or most commonly used language is the specified language.
- 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_69ca84c99e34819092e5563a7106cfca |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9ccf21a08190a1302b933b9e50be |
completed | April 1, 2026, 10:31 p.m. |
| PD | Predicate disambiguation | batch_69ccd5b840f081909f66bf0b66d17d9b |
completed | April 1, 2026, 8:22 a.m. |
Created at: March 30, 2026, 8:16 p.m.