Triple
T2448778
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arethusa class (1930s) |
E53653
|
entity |
| Predicate | numberOfShips |
P39920
|
FINISHED |
| Object | 4 |
—
|
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: 4 | Statement: [Arethusa class (1930s), numberOfShips, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfShips Context triple: [Arethusa class (1930s), numberOfShips, 4]
-
A.
numberOfShipsInvolved
Indicates the total count of ships that participated or were involved in a specified event or situation.
-
B.
numberOfPlannedShips
Indicates the total count of ships that are intended or scheduled to be built, deployed, or used according to a plan.
-
C.
numberOfNaves
Indicates the specific count of naves (longitudinal sections) that a building, typically a church, possesses.
-
D.
otherShipsInFleet
Indicates that the subject ship and the object ship are distinct members of the same fleet.
-
E.
navalFleet
Indicates a relationship where multiple naval vessels are organized and operate together as a coordinated maritime military force.
- 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_69ab495d227c8190b26ae6548eeb1019 |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd2bc7b5481908b3664495e99f1a4 |
completed | March 7, 2026, 7:24 a.m. |
| PD | Predicate disambiguation | batch_69abd0aed2688190a18fe66b98d80e0b |
completed | March 7, 2026, 7:15 a.m. |
| PDg | Predicate description generation | batch_69abd2baee308190bdaa41ef1f6bc9cc |
completed | March 7, 2026, 7:24 a.m. |
Created at: March 6, 2026, 9:43 p.m.