Triple
T2676620
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Lagos (1693) |
E56475
|
entity |
| Predicate | convoySize |
P39920
|
FINISHED |
| Object | around 200 merchant ships |
—
|
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: around 200 merchant ships | Statement: [Battle of Lagos (1693), convoySize, around 200 merchant ships]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: convoySize Context triple: [Battle of Lagos (1693), convoySize, around 200 merchant ships]
-
A.
convoyCargo
Indicates that one entity is transporting or escorting another entity as cargo within a convoy.
-
B.
fleetSize
Indicates the total number of vehicles, vessels, or units that collectively make up a fleet associated with an entity.
-
C.
squadronSize
Indicates the number of units or members that make up a particular squadron.
-
D.
numberOfShips
chosen
Indicates the quantity of ships associated with a given entity or situation.
-
E.
numberOfShipsInvolved
Indicates the total count of ships that participated or were involved in a specified event 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_69ab4a4b13fc81909dfdb3f23da46832 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abda2f7bf88190a1e3103dd014d871 |
completed | March 7, 2026, 7:56 a.m. |
| PD | Predicate disambiguation | batch_69abd81ab9d08190b72b6104c6dbc769 |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:54 p.m.