Triple
T7538008
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Presidente Nicolau Lobato International Airport |
E178197
|
entity |
| Predicate | hasCommonLanguagesAtFacility |
P741
|
FINISHED |
| Object | Indonesian |
—
|
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: Indonesian | Statement: [Presidente Nicolau Lobato International Airport, hasCommonLanguagesAtFacility, Indonesian]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCommonLanguagesAtFacility Context triple: [Presidente Nicolau Lobato International Airport, hasCommonLanguagesAtFacility, Indonesian]
-
A.
hasLanguages
Indicates that an entity is associated with one or more languages it uses, supports, or is expressed in.
-
B.
eligibleLanguage
Indicates that a particular language satisfies the required conditions to be considered valid or allowed in a given context.
-
C.
languagesSpoken
chosen
Indicates that an entity is able to communicate using one or more specified languages.
-
D.
usesWorkingLanguagesOf
Indicates that one entity employs or operates using the working languages associated with another entity.
-
E.
hasLanguageOfSurroundingCountries
Indicates that an entity uses or includes the languages commonly spoken in the countries that geographically surround it.
- 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_69c69f2be3888190a6667a27f8f195e9 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f8710400819088e430c8c550577e |
completed | March 27, 2026, 9:36 p.m. |
| PD | Predicate disambiguation | batch_69c6f4d8eedc81908c1ae421e0e63798 |
completed | March 27, 2026, 9:21 p.m. |
Created at: March 27, 2026, 3:48 p.m.