Triple
T26522435
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Montevideo Maru |
E669993
|
entity |
| Predicate | numberOfPeopleOnBoardApproximate |
P111137
|
FINISHED |
| Object | about 1050 |
—
|
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: about 1050 | Statement: [Montevideo Maru, numberOfPeopleOnBoardApproximate, about 1050]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfPeopleOnBoardApproximate Context triple: [Montevideo Maru, numberOfPeopleOnBoardApproximate, about 1050]
-
A.
totalPeopleOnBoard
Indicates the total number of people currently present on a given vehicle, vessel, or similar conveyance.
-
B.
passengersCountApproximate
Indicates that the number of passengers involved is given as an approximate or estimated count rather than an exact figure.
-
C.
crewCountApproximate
Indicates that the relationship specifies an estimated or approximate number of crew members associated with an entity.
-
D.
crewAndPassengersCount
Indicates the total number of people on a vehicle or vessel, combining both crew members and passengers.
-
E.
hasApproximateNumberOfPeople
chosen
Indicates that an entity is associated with an estimated or approximate count of people, rather than an exact number.
- 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_69eeb31b6dcc8190b30632dc3928a0c0 |
completed | April 27, 2026, 12:51 a.m. |
| NER | Named-entity recognition | batch_69fde5d7d9548190880a9d95b8f0f66b |
completed | May 8, 2026, 1:32 p.m. |
| PD | Predicate disambiguation | batch_69fde4e1bf9c81909754545275eccc03 |
completed | May 8, 2026, 1:28 p.m. |
Created at: April 27, 2026, 1:29 a.m.