Triple
T12535731
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stowmarket |
E299683
|
entity |
| Predicate | hasFireworksDisasterHistory |
P15089
|
FINISHED |
| Object | 1871 gun cotton explosion near Stowmarket |
—
|
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: 1871 gun cotton explosion near Stowmarket | Statement: [Stowmarket, hasFireworksDisasterHistory, 1871 gun cotton explosion near Stowmarket]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFireworksDisasterHistory Context triple: [Stowmarket, hasFireworksDisasterHistory, 1871 gun cotton explosion near Stowmarket]
-
A.
hasFireHistory
Indicates that an entity has experienced one or more fire events in the past.
-
B.
hasDisaster
chosen
Indicates that an entity experiences, is affected by, or is associated with a disaster event.
-
C.
hasPyrotechnics
Indicates that an entity includes, uses, or features pyrotechnic effects or fireworks as part of its characteristics or activities.
-
D.
hasFirecrackerBan
Indicates that a governing body or authority has enacted a prohibition on the use, sale, or possession of firecrackers.
-
E.
hasFrequentExplosions
Indicates that the subject regularly experiences or produces explosions occurring at short or recurring intervals.
- 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_69d6ada707008190aaec1238117c9379 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d95f5507b481908d13cc317b7402f6 |
completed | April 10, 2026, 8:36 p.m. |
| PD | Predicate disambiguation | batch_69d9540d7b788190a0d57b098e90e491 |
completed | April 10, 2026, 7:48 p.m. |
Created at: April 8, 2026, 9:57 p.m.