Triple
T25379278
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Camp Schwab |
E631337
|
entity |
| Predicate | hasRunwayPlan |
P158820
|
FINISHED |
| Object | V-shaped offshore runway configuration |
—
|
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: V-shaped offshore runway configuration | Statement: [Camp Schwab, hasRunwayPlan, V-shaped offshore runway configuration]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRunwayPlan Context triple: [Camp Schwab, hasRunwayPlan, V-shaped offshore runway configuration]
-
A.
hasRunwayPresence
Indicates that an entity maintains a physical runway or landing strip suitable for aircraft operations.
-
B.
hasRunwayNumber
Indicates that an airport or airfield runway is assigned a specific identifying number.
-
C.
hasRunwayCount
Indicates the number of runways that a given entity (such as an airport) possesses.
-
D.
hasRunwayType
Indicates that an airport or airfield has a runway of a specified type or surface classification.
-
E.
hasRunwayUse
Indicates that a particular runway is authorized or designated for use by a specific aircraft, operation, or purpose.
- 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_69e75a8c50788190aabaa9f96710fc43 |
completed | April 21, 2026, 11:07 a.m. |
| NER | Named-entity recognition | batch_69f55e5e4b0081909b64940d85d636f5 |
completed | May 2, 2026, 2:15 a.m. |
| PD | Predicate disambiguation | batch_69f4806d93dc8190b9dff4c63186faff |
completed | May 1, 2026, 10:29 a.m. |
| PDg | Predicate description generation | batch_69f48b9058d081908ec9af261ee092e2 |
completed | May 1, 2026, 11:16 a.m. |
Created at: April 21, 2026, 1:46 p.m.