Triple
T780354
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frankfurt am Main |
E16481
|
entity |
| Predicate | airportRank |
P19832
|
FINISHED |
| Object | one of Europe’s busiest airports |
—
|
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 Europe’s busiest airports | Statement: [Frankfurt am Main, airportRank, one of Europe’s busiest airports]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: airportRank Context triple: [Frankfurt am Main, airportRank, one of Europe’s busiest airports]
-
A.
areaRank
Indicates the relative ordering or position of an entity based on the size of its area compared to others.
-
B.
passengerTrafficRankUS
Indicates the relative ranking of a location or facility within the United States based on the volume of passenger traffic it handles.
-
C.
peakPassengerTrafficRank
Indicates the relative position of an entity in an ordered list based on the amount of passenger traffic it experiences at its peak.
-
D.
hubAirport
Indicates that an airport serves as a primary hub or central operating base for a particular airline or carrier.
-
E.
largestAirport
Indicates that one airport is the largest (typically by area, traffic, or capacity) among a specified set or within a given region.
- 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_69a4936ad1fc81908f190208059ccf78 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a90365648190ace53b0f0e87aa68 |
completed | March 1, 2026, 9 p.m. |
| PD | Predicate disambiguation | batch_69a4a50bd23081908908235b8ec9201e |
completed | March 1, 2026, 8:43 p.m. |
| PDg | Predicate description generation | batch_69a4a8f09d108190b8c83a6169d65c0c |
completed | March 1, 2026, 9 p.m. |
Created at: March 1, 2026, 7:37 p.m.