Triple
T923446
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Royal Indian Navy mutiny |
E19931
|
entity |
| Predicate | numberOfShoreEstablishmentsInvolved |
P21578
|
FINISHED |
| Object | about 20 shore establishments |
—
|
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 20 shore establishments | Statement: [Royal Indian Navy mutiny, numberOfShoreEstablishmentsInvolved, about 20 shore establishments]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfShoreEstablishmentsInvolved Context triple: [Royal Indian Navy mutiny, numberOfShoreEstablishmentsInvolved, about 20 shore establishments]
-
A.
numberOfShipsInvolved
Indicates the total count of ships that participated or were involved in a specified event or situation.
-
B.
shorelineLength
Indicates the total measured extent of a land area’s boundary where it meets a body of water.
-
C.
cityBeachNumber
Indicates the number of beaches associated with a given city.
-
D.
numberOfVenues
Indicates the total count of venues associated with a given entity or context.
-
E.
hasShorelineUse
Indicates that a geographic area or property is used for a particular type of activity or purpose along its shoreline.
- 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_69a493a099788190a696d9d8408cbaf4 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b314f6fc81908a3ccc2e741e3c2b |
completed | March 1, 2026, 9:43 p.m. |
| PD | Predicate disambiguation | batch_69a4b295b02481908e5f53bfcb83cc94 |
completed | March 1, 2026, 9:41 p.m. |
| PDg | Predicate description generation | batch_69a4b30efd2c8190b780a6dee086d0aa |
completed | March 1, 2026, 9:43 p.m. |
Created at: March 1, 2026, 7:40 p.m.