Triple
T180807
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Manchester Airport |
E3870
|
entity |
| Predicate | hasPassengerTrafficRankInUK |
P1667
|
FINISHED |
| Object | one of the busiest |
—
|
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 | Statement: [Manchester Airport, hasPassengerTrafficRankInUK, one of the busiest]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPassengerTrafficRankInUK Context triple: [Manchester Airport, hasPassengerTrafficRankInUK, one of the busiest]
-
A.
peakPassengerTrafficRank
chosen
Indicates the relative position of an entity in an ordered list based on the amount of passenger traffic it experiences at its peak.
-
B.
hasPopulationRankInUK
Indicates the relative position of an entity’s population size compared to other entities within the United Kingdom.
-
C.
peakFreightTrafficRank
Indicates the relative ranking position of an entity based on the highest level of freight traffic it experiences or handles compared to others.
-
D.
hasMajorRailwayStation
Indicates that a place contains or is served by a principal railway station that functions as a major hub for rail transport.
-
E.
trafficLevel
Indicates the degree of congestion or flow intensity present in a transportation network or route at a given time.
- 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_69a25497e2f08190a040f8c6e1842643 |
completed | Feb. 28, 2026, 2:36 a.m. |
| NER | Named-entity recognition | batch_69a25901a9188190b8f510bec8c8e7f2 |
completed | Feb. 28, 2026, 2:54 a.m. |
| PD | Predicate disambiguation | batch_69a2566ccc288190add5624ede96d82b |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:40 a.m.