Triple
T29434287
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Noida International Airport |
E746523
|
entity |
| Predicate | plannedRunwayCountUltimate |
P19671
|
FINISHED |
| Object | 5 |
—
|
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: 5 | Statement: [Noida International Airport, plannedRunwayCountUltimate, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: plannedRunwayCountUltimate Context triple: [Noida International Airport, plannedRunwayCountUltimate, 5]
-
A.
numberOfRunways
Indicates the quantity of runways associated with a given entity, such as an airport or airfield.
-
B.
hasRunwayCount
chosen
Indicates the number of runways that a given entity (such as an airport) possesses.
-
C.
hasParallelRunway
Indicates that one runway is parallel in orientation and alignment to another runway.
-
D.
runwaysUsableAt
Indicates that certain runways at a location are available and suitable for use (e.g., for takeoff or landing) at a given time or under specified conditions.
-
E.
runwayPlan
Indicates a planned or designated use of a runway for aircraft operations (such as takeoff, landing, or sequencing) within an airfield’s operational schedule.
- 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_69f0a7a180e48190ae775e40047dbcb5 |
completed | April 28, 2026, 12:27 p.m. |
| NER | Named-entity recognition | batch_69f72921cf2c8190909bb53f78bcc890 |
completed | May 3, 2026, 10:53 a.m. |
| PD | Predicate disambiguation | batch_69f7283d8cec8190b524c144948bc4ec |
completed | May 3, 2026, 10:49 a.m. |
Created at: April 28, 2026, 3:15 p.m.