Triple
T1765038
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Noi Bai International Airport |
E38742
|
entity |
| Predicate | rankingInCountryBySize |
P31652
|
FINISHED |
| Object | one of the largest airports in Vietnam |
—
|
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: one of the largest airports in Vietnam | Statement: [Noi Bai International Airport, rankingInCountryBySize, one of the largest airports in Vietnam]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankingInCountryBySize Context triple: [Noi Bai International Airport, rankingInCountryBySize, one of the largest airports in Vietnam]
-
A.
rankInWorldByArea
Indicates the position of an entity in a global ordering based on its total area size.
-
B.
countryRanking
Indicates the relative position or rank assigned to a country within a specific ordered list or comparative evaluation.
-
C.
populationRank
Indicates the relative position of an entity in an ordered list based on the size of its population.
-
D.
continentRankByArea
Indicates the relative position of a continent in an ordered list based on its total land area.
-
E.
continentRankByPopulation
Indicates the relative position of a continent in an ordered list based on its population size.
- F. None of above. chosen
Provenance (4 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_69a8862d562481908d7025a1c1f67c0d |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab39fc2c448190bfaf1ee8d474632a |
completed | March 6, 2026, 8:33 p.m. |
| PD | Predicate disambiguation | batch_69aa61cbb1288190a7ba38b61905f578 |
completed | March 6, 2026, 5:10 a.m. |
| PDg | Predicate description generation | batch_69ab39faf69c8190bae98d3e3911078f |
completed | March 6, 2026, 8:32 p.m. |
Created at: March 4, 2026, 7:31 p.m.