Triple
T15000548
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dar Pomorza |
E374074
|
entity |
| Predicate | convertedToMuseumShip |
P60408
|
FINISHED |
| Object | 1983 |
—
|
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: 1983 | Statement: [Dar Pomorza, convertedToMuseumShip, 1983]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: convertedToMuseumShip Context triple: [Dar Pomorza, convertedToMuseumShip, 1983]
-
A.
hasMuseumShip
chosen
Indicates that one entity possesses or is associated with a museum ship, typically a preserved vessel displayed for public exhibition.
-
B.
conversionOfShip
Indicates the process by which a ship is altered or transformed from one configuration, role, or specification into another.
-
C.
convertedToMausoleum
Indicates that something has been transformed from its original state or use into a mausoleum, typically for the purpose of serving as a burial or memorial structure.
-
D.
acquiredAsTrainingShip
Indicates that one entity obtained another specifically for use as a training ship.
-
E.
originalShipType
Indicates the type or category of ship that an entity was originally classified or built as.
- 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_69d85ccc84388190aa151e5173370c8d |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded72fec948190b1c9705538c57976 |
completed | April 15, 2026, 12:09 a.m. |
| PD | Predicate disambiguation | batch_69de9a6531a88190acde65199a477350 |
completed | April 14, 2026, 7:49 p.m. |
Created at: April 10, 2026, 2:54 a.m.