Triple
T20685652
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DPS |
E508407
|
entity |
| Predicate | passengerTrafficRankInIndonesia |
P25678
|
FINISHED |
| Object | one of the busiest airports in Indonesia |
—
|
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 busiest airports in Indonesia | Statement: [DPS, passengerTrafficRankInIndonesia, one of the busiest airports in Indonesia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: passengerTrafficRankInIndonesia Context triple: [DPS, passengerTrafficRankInIndonesia, one of the busiest airports in Indonesia]
-
A.
populationRankInIndonesia
Indicates the relative position of an entity in terms of population size compared to other entities within Indonesia.
-
B.
passengerTrafficRankingWorld
Indicates the relative position of an entity in a global ranking based on the volume of passenger traffic it handles.
-
C.
hasPassengerTrafficRank
chosen
Indicates the relative position or ranking of an entity based on the volume of passenger traffic it handles compared to others.
-
D.
peakPassengerTrafficRank
Indicates the relative position of an entity in an ordered list based on the amount of passenger traffic it experiences at its peak.
-
E.
passengerTraffic
Indicates the flow or volume of passengers moving through or using a particular transport service, route, or facility.
- 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_69e0b4c1ed408190b72dd26b1e33f8a1 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6beabf72881909771b6c6a81276d6 |
completed | April 21, 2026, 12:02 a.m. |
| PD | Predicate disambiguation | batch_69e5c03caee881908be4dd25796a03d5 |
completed | April 20, 2026, 5:57 a.m. |
Created at: April 16, 2026, 11:45 a.m.