Triple
T25430871
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FUN |
E637248
|
entity |
| Predicate | airportImportance |
P71786
|
FINISHED |
| Object | primary international connection for 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: primary international connection for Tuvalu | Statement: [FUN, airportImportance, primary international connection for Tuvalu]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: airportImportance Context triple: [FUN, airportImportance, primary international connection for Tuvalu]
-
A.
relativeImportanceAtAirport
chosen
Indicates the comparative level of importance or priority assigned to entities within the context of an airport.
-
B.
portImportance
Indicates the relative significance or strategic value of a port within a given context (such as trade, logistics, or transportation networks).
-
C.
airportRank
Indicates the relative position or level assigned to an airport within a ranking or ordered list.
-
D.
rankingByAircraftMovements
Indicates the relative order of entities based on the number of aircraft movements (takeoffs and landings) they handle.
-
E.
passengerTrafficRankUS
Indicates the relative ranking of a location or facility within the United States based on the volume of passenger traffic it handles.
- 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_69f627aedf548190bc9f53c8a2d67b50 |
completed | May 2, 2026, 4:34 p.m. |
| PD | Predicate disambiguation | batch_69f623a4e1048190bbb8dd1253fdcee9 |
completed | May 2, 2026, 4:17 p.m. |
Created at: April 21, 2026, 1:58 p.m.