Triple
T2337528
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hamburg Airport |
E44344
|
entity |
| Predicate | hasPassengerTerminalFacilities |
P38302
|
FINISHED |
| Object | shops |
—
|
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: shops | Statement: [Hamburg Airport, hasPassengerTerminalFacilities, shops]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPassengerTerminalFacilities Context triple: [Hamburg Airport, hasPassengerTerminalFacilities, shops]
-
A.
hasPassengerTerminal
Indicates that one entity possesses or is equipped with a passenger terminal used for boarding, alighting, or handling passengers.
-
B.
hasPassengerTerminalDesign
Indicates a design relationship in which one entity specifies or defines the passenger terminal layout, structure, or configuration of another entity.
-
C.
hasCargoTerminal
Indicates that a location or facility includes or is equipped with a cargo terminal for handling freight.
-
D.
hasRailFacility
Indicates that an entity possesses or is served by a rail-related facility, such as a railway station, terminal, or yard.
-
E.
hasPassengerServicesTo
Indicates that a transportation provider operates passenger services connecting one location or entity to another.
- 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_69a889132b488190bbb43ad4780ddd92 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abc6f75d888190a2e41edaa532e83f |
completed | March 7, 2026, 6:34 a.m. |
| PD | Predicate disambiguation | batch_69abc594087c819098100a10c5478a4b |
completed | March 7, 2026, 6:28 a.m. |
| PDg | Predicate description generation | batch_69abc6f4245881909282b3184a288e2a |
completed | March 7, 2026, 6:34 a.m. |
Created at: March 4, 2026, 7:51 p.m.