Triple
T29105249
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jumbo Mark II |
E736744
|
entity |
| Predicate | numberOfVessels |
P39920
|
FINISHED |
| Object | 3 |
—
|
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: 3 | Statement: [Jumbo Mark II, numberOfVessels, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfVessels Context triple: [Jumbo Mark II, numberOfVessels, 3]
-
A.
numberOfShips
chosen
Indicates the quantity of ships associated with a given entity or situation.
-
B.
namedVesselOf
Indicates that one entity is the specific named vessel (e.g., ship, boat, or craft) associated with or belonging to another entity.
-
C.
hasVessel
Indicates that one entity possesses, uses, or is associated with a particular vessel (such as a container, ship, or transport medium) in the context of the described relationship or action.
-
D.
numberOfShipsInvolved
Indicates the total count of ships that participated or were involved in a specified event or situation.
-
E.
classShipCount
Indicates the number of ships associated with a particular class or category.
- 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_69f077ec765c81909474c88bcc8bab43 |
completed | April 28, 2026, 9:03 a.m. |
| NER | Named-entity recognition | batch_69ffc89596d08190b97bd60b45c7f9c0 |
completed | May 9, 2026, 11:51 p.m. |
| PD | Predicate disambiguation | batch_69ffc81ba5dc8190ae94d44e2284948f |
completed | May 9, 2026, 11:49 p.m. |
Created at: April 28, 2026, 11:15 a.m.