Triple
T3213462
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Port Harcourt International Airport |
E67334
|
entity |
| Predicate | hasPassengerBoardingGates |
P46229
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Port Harcourt International Airport, hasPassengerBoardingGates, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPassengerBoardingGates Context triple: [Port Harcourt International Airport, hasPassengerBoardingGates, yes]
-
A.
hasBoardingGatesFor
Indicates that a location or facility provides designated boarding gates used for embarking passengers onto specific transportation services (such as flights or trains).
-
B.
numberOfGates
Indicates the quantity of gates associated with or belonging to an entity.
-
C.
hasBoardingAreaFor
Indicates that one entity provides or contains a designated area where passengers can board another entity (such as a vehicle or vessel).
-
D.
hasPassengerTerminal
Indicates that one entity possesses or is equipped with a passenger terminal used for boarding, alighting, or handling passengers.
-
E.
hasPassengerTerminalFacilities
Indicates that an entity provides facilities or infrastructure specifically intended for handling and serving passengers.
- 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_69ad858ac36c81909962589cd277d6e2 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaabba8e481909118d9f888ddcd63 |
completed | March 8, 2026, 4:58 p.m. |
| PD | Predicate disambiguation | batch_69ad9e09b83881908801d79c3d9254f9 |
completed | March 8, 2026, 4:04 p.m. |
| PDg | Predicate description generation | batch_69ada0f9259c8190afbc5ad0fa55436b |
completed | March 8, 2026, 4:16 p.m. |
Created at: March 8, 2026, 3:07 p.m.