Triple
T1581053
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Colorado-class battleship |
E33761
|
entity |
| Predicate | armourConningTower |
P6071
|
FINISHED |
| Object | up to 16 inches |
—
|
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: up to 16 inches | Statement: [Colorado-class battleship, armourConningTower, up to 16 inches]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: armourConningTower Context triple: [Colorado-class battleship, armourConningTower, up to 16 inches]
-
A.
turret
Indicates that an entity is equipped with or associated with a turret, typically a rotating weapon or defense mechanism.
-
B.
armour
chosen
Indicates that an entity provides protective covering or defense for another entity.
-
C.
armourBelt
Indicates a relationship where an armour belt is equipped on, attached to, or associated with an entity (such as a character, vehicle, or structure) as protective gear.
-
D.
turretCrew
Indicates that an entity serves as a crew member operating or manning a turret associated with another entity.
-
E.
hasTower
Indicates that one entity possesses, contains, or is characterized by the presence of a tower.
- 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_69a885f27a4c8190a4622252cdf54c00 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69abacfb1144819080c5687175aba1e1 |
completed | March 7, 2026, 4:43 a.m. |
| PD | Predicate disambiguation | batch_69aa61b0f5bc8190b1dc272990a59c13 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:27 p.m.