Triple
T91524
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Terminal 5 |
E1837
|
entity |
| Predicate | hasBaggageSystem |
P4367
|
FINISHED |
| Object | checked baggage handling system |
—
|
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: checked baggage handling system | Statement: [Terminal 5, hasBaggageSystem, checked baggage handling system]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBaggageSystem Context triple: [Terminal 5, hasBaggageSystem, checked baggage handling system]
-
A.
hasLiftType
Indicates the specific type or category of lift associated with an entity.
-
B.
hasCargoTerminal
Indicates that a location or facility includes or is equipped with a cargo terminal for handling freight.
-
C.
hasSeating
Indicates that one entity provides or contains seating capacity or seating arrangements for another entity.
-
D.
carriedBy
Indicates that one entity is physically supported and transported by another entity.
-
E.
aircraftType
Indicates the specific model or category of aircraft associated with an entity or event.
- 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_69a24d1a97dc819094e6c021fe9b05a7 |
completed | Feb. 28, 2026, 2:04 a.m. |
| NER | Named-entity recognition | batch_69a2512ef600819084d3c627f0d534f4 |
completed | Feb. 28, 2026, 2:21 a.m. |
| PD | Predicate disambiguation | batch_69a24eb9a5ac8190b1d1300e8c4e3606 |
completed | Feb. 28, 2026, 2:11 a.m. |
| PDg | Predicate description generation | batch_69a2512cc3108190aefe5e624312f7d0 |
completed | Feb. 28, 2026, 2:21 a.m. |
Created at: Feb. 28, 2026, 2:07 a.m.