Triple
T23280624
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | USS Cairo |
E588849
|
entity |
| Predicate | statusAfterSinking |
P76414
|
FINISHED |
| Object | abandoned and later buried in river mud |
—
|
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: abandoned and later buried in river mud | Statement: [USS Cairo, statusAfterSinking, abandoned and later buried in river mud]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: statusAfterSinking Context triple: [USS Cairo, statusAfterSinking, abandoned and later buried in river mud]
-
A.
sunkAs
Indicates that one entity caused another entity to sink or become submerged, typically resulting in its loss or destruction.
-
B.
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.
-
C.
sunkOff
Indicates that one entity was sunk at a location situated off (near but not directly at) another referenced place or feature.
-
D.
sinkingConsequence
chosen
Indicates the outcome or effect that results from something sinking.
-
E.
sankOn
Indicates that one entity moved downward and became submerged or lower in level relative to another entity or reference point.
- 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_69e25d16e2c08190a291de254703129e |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f19642b46481909fd455acd2155792 |
completed | April 29, 2026, 5:25 a.m. |
| PD | Predicate disambiguation | batch_69effcecabd88190856fb6e1d993e4dd |
completed | April 28, 2026, 12:18 a.m. |
Created at: April 17, 2026, 4:55 p.m.