Triple
T37523019
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | HMS Queen Charlotte (1790) |
E932829
|
entity |
| Predicate | gunCountCategory |
P188202
|
FINISHED |
| Object | three-decker first rate |
—
|
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: three-decker first rate | Statement: [HMS Queen Charlotte (1790), gunCountCategory, three-decker first rate]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: gunCountCategory Context triple: [HMS Queen Charlotte (1790), gunCountCategory, three-decker first rate]
-
A.
numberOfGuns
Indicates the quantity of guns associated with a given entity or situation.
-
B.
gunType
Indicates the specific category or kind of gun associated with an entity.
-
C.
gunCalibre
Indicates the relationship between a firearm and the calibre (size/diameter) of ammunition it is designed to use.
-
D.
weaponCategory
Indicates the classification or type of weapon to which an item or armament belongs.
-
E.
gunProduction
Indicates the relationship where an entity manufactures, assembles, or otherwise produces guns or firearms.
- 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_69f76ec8862c8190bfa24145f5480642 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fba5eec0448190a5e6f0c43fdcd0e3 |
completed | May 6, 2026, 8:34 p.m. |
| PD | Predicate disambiguation | batch_69fba34edd548190bfa980e6e16e0a88 |
completed | May 6, 2026, 8:23 p.m. |
| PDg | Predicate description generation | batch_69fba5ee00fc81909be7b947a3f95034 |
completed | May 6, 2026, 8:34 p.m. |
Created at: May 3, 2026, 4:17 p.m.