Triple
T36217313
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | French Navy ships of the line |
E1047733
|
entity |
| Predicate | typicalArmamentRange |
P204797
|
FINISHED |
| Object | 50 to 120 guns |
—
|
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: 50 to 120 guns | Statement: [French Navy ships of the line, typicalArmamentRange, 50 to 120 guns]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalArmamentRange Context triple: [French Navy ships of the line, typicalArmamentRange, 50 to 120 guns]
-
A.
armamentCapacity
Indicates the maximum quantity or type of weapons or munitions that something is designed or allowed to carry.
-
B.
artilleryRange
Indicates that one entity is within the effective firing distance of another entity’s artillery.
-
C.
hasFiringRange
Indicates that one entity possesses or provides a designated area or capability for discharging weapons over a specified distance.
-
D.
primaryArmament
Indicates the main weapon or principal offensive system that an entity (such as a vehicle, vessel, or platform) is equipped with or uses.
-
E.
typicalWeapon
Indicates that the object is a weapon commonly or characteristically used by the subject.
- 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_69f76e42c878819095c8d19c0267fb87 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0a54cc8190868c1bfa1590d1a6 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c82f8c88190bd77a086023ac0e1 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:09 p.m.