Triple
T25430863
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FUN |
E637248
|
entity |
| Predicate | airportSecondaryUse |
P135806
|
FINISHED |
| Object | domestic flights within Tuvalu |
—
|
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: domestic flights within Tuvalu | Statement: [FUN, airportSecondaryUse, domestic flights within Tuvalu]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: airportSecondaryUse Context triple: [FUN, airportSecondaryUse, domestic flights within Tuvalu]
-
A.
airportUse
Indicates that an airport is used or utilized by a particular entity, such as an airline, organization, or service.
-
B.
airportRole
Indicates that an entity serves a specific functional role or capacity within the context of an airport.
-
C.
airportFeature
chosen
Indicates that an airport possesses or is characterized by a particular feature, facility, or attribute.
-
D.
airportSpecialization
Indicates that an airport is specialized or designated for a particular primary function, service type, or category of operations.
-
E.
hasSecondaryAirport
Indicates that an entity is associated with an additional, typically smaller or alternative, airport beyond its primary one.
- 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_69e75db58a1c8190891b9ff7c2f8414e |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f5ffc74fa481909b4fe24a9337f9eb |
completed | May 2, 2026, 1:44 p.m. |
| PD | Predicate disambiguation | batch_69f5f7f99dc08190afcfb3bc4dfbec1d |
completed | May 2, 2026, 1:11 p.m. |
Created at: April 21, 2026, 1:58 p.m.