Triple
T17072232
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Francisco Maru |
E414250
|
entity |
| Predicate | sunkMonth |
P125748
|
FINISHED |
| Object | February |
—
|
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: February | Statement: [San Francisco Maru, sunkMonth, February]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sunkMonth Context triple: [San Francisco Maru, sunkMonth, February]
-
A.
sunkAs
Indicates that one entity caused another entity to sink or become submerged, typically resulting in its loss or destruction.
-
B.
sunk
Indicates that one entity caused another entity to go below the surface of a liquid, typically water, so that it is submerged or destroyed.
-
C.
sunkBy
Indicates that one entity (typically a vessel or structure) was caused to sink or be destroyed in water by another entity.
-
D.
sunkDuring
Indicates that one entity was sunk in the course of, or as a result of, the event or time period represented by another entity.
-
E.
sunkOff
Indicates that one entity was sunk at a location situated off (near but not directly at) another referenced place or feature.
- 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_69d886cef44c8190ba56c44b4e863e64 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dbc1b7d48190979a848b4188cb22 |
completed | April 18, 2026, 7:30 p.m. |
| PD | Predicate disambiguation | batch_69e35d642f74819098c014135e249b27 |
completed | April 18, 2026, 10:31 a.m. |
| PDg | Predicate description generation | batch_69e3753f93c88190808fec5692f66699 |
completed | April 18, 2026, 12:12 p.m. |
Created at: April 10, 2026, 5:34 a.m.