Triple
T4074689
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SPJC |
E86733
|
entity |
| Predicate | associatedAirportRole |
P5148
|
FINISHED |
| Object | primary airport for Lima metropolitan area |
—
|
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: primary airport for Lima metropolitan area | Statement: [SPJC, associatedAirportRole, primary airport for Lima metropolitan area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedAirportRole Context triple: [SPJC, associatedAirportRole, primary airport for Lima metropolitan area]
-
A.
airportRole
chosen
Indicates that an entity serves a specific functional role or capacity within the context of an airport.
-
B.
airportLineRole
Indicates the specific functional role or responsibility an entity has in relation to an airport transit line or route.
-
C.
associatedWithAirportType
Indicates that an entity has a connection or linkage to a specific category or type of airport.
-
D.
associatedAirportServes
Indicates that a given airport provides service to, or is used by, the associated entity (such as a city, region, or facility).
-
E.
associatedWithAirportName
Indicates a relationship where an entity is linked or connected to a specific airport by its name.
- 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_69aed93ebe448190a1f1686e28740ac9 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefc25e2e08190b3c048e1b8f85bbf |
completed | March 9, 2026, 4:58 p.m. |
| PD | Predicate disambiguation | batch_69aef9061d2481908307cafc9e9b32c0 |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:39 p.m.