Triple
T15852109
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cardington, Bedfordshire |
E384365
|
entity |
| Predicate | airshipShedsUsedFor |
P22186
|
FINISHED |
| Object | construction of R100 airship |
—
|
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: construction of R100 airship | Statement: [Cardington, Bedfordshire, airshipShedsUsedFor, construction of R100 airship]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: airshipShedsUsedFor Context triple: [Cardington, Bedfordshire, airshipShedsUsedFor, construction of R100 airship]
-
A.
airshipClass
Indicates the classification or type category to which an airship belongs.
-
B.
airshipNumber
Indicates the identifying number assigned to a specific airship within a set or system.
-
C.
hasHangars
chosen
Indicates that one entity possesses or contains hangars used for housing aircraft or similar vehicles.
-
D.
airshipBoss
Indicates that one entity serves as the primary controlling or commanding figure over an airship-related context or scenario involving another entity.
-
E.
theaterOfUseOfAircraft
Indicates the geographic or operational area in which an aircraft is intended to be or is actually employed.
- 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_69d86da422088190aac39e32e6c68429 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e174de2cd48190ab18e48c9f051a2a |
completed | April 16, 2026, 11:46 p.m. |
| PD | Predicate disambiguation | batch_69e142b976c081908d3ba3e705419f3a |
completed | April 16, 2026, 8:12 p.m. |
Created at: April 10, 2026, 4:50 a.m.