Triple
T5243245
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Soesterberg |
E118394
|
entity |
| Predicate | hasReuseOfAirBase |
P62349
|
FINISHED |
| Object | business park |
—
|
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: business park | Statement: [Soesterberg, hasReuseOfAirBase, business park]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasReuseOfAirBase Context triple: [Soesterberg, hasReuseOfAirBase, business park]
-
A.
isAirBase
Indicates that the subject entity functions as a military air base, i.e., a facility used for operating, housing, and supporting aircraft and related activities.
-
B.
hasBasedAircraft
Indicates that an aircraft is regularly stationed or primarily based at a particular location or facility.
-
C.
hasRunwayUse
Indicates that a particular runway is authorized or designated for use by a specific aircraft, operation, or purpose.
-
D.
hasAirspace
Indicates that one entity possesses, controls, or is associated with a defined region of airspace relative to another entity or area.
-
E.
previousAircraftUsed
Indicates that one aircraft was used immediately before another in a sequence of aircraft usage.
- 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_69bd4468aacc8190a8196f71855cdf4f |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7b4da7308190856cdcee9cca41eb |
completed | March 20, 2026, 4:52 p.m. |
| PD | Predicate disambiguation | batch_69bd77c1397c8190a7fd844d7a396e54 |
completed | March 20, 2026, 4:37 p.m. |
| PDg | Predicate description generation | batch_69bd79e9d794819097bb628c603d14af |
completed | March 20, 2026, 4:46 p.m. |
Created at: March 20, 2026, 1:49 p.m.