Triple
T4638015
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Japanese battlecruiser Amagi |
E101580
|
entity |
| Predicate | shipClassCount |
P43019
|
FINISHED |
| Object | four planned ships in Amagi class |
—
|
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: four planned ships in Amagi class | Statement: [Japanese battlecruiser Amagi, shipClassCount, four planned ships in Amagi class]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: shipClassCount Context triple: [Japanese battlecruiser Amagi, shipClassCount, four planned ships in Amagi class]
-
A.
shipClass
Indicates the classification or type category to which a particular ship belongs.
-
B.
classShipCount
chosen
Indicates the number of ships associated with a particular class or category.
-
C.
tonnageClass
Indicates a classification relationship where an entity is assigned to a category based on its tonnage (weight or carrying capacity range).
-
D.
shipTypeProduced
Indicates that a particular type of ship is produced, built, or manufactured by a given entity.
-
E.
numberOfShips
Indicates the quantity of ships associated with a given entity or situation.
- 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_69bd43d3bc7c81908f81fcf380476b0f |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd5a64214481908a207e8070cc7a45 |
completed | March 20, 2026, 2:32 p.m. |
| PD | Predicate disambiguation | batch_69bd5233cb5081908807e2b150f0ca06 |
completed | March 20, 2026, 1:57 p.m. |
Created at: March 20, 2026, 1:13 p.m.