Triple
T13330501
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | USS Spiegel Grove |
E317559
|
entity |
| Predicate | sankAsArtificialReef |
P109553
|
FINISHED |
| Object | 2002 |
—
|
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: 2002 | Statement: [USS Spiegel Grove, sankAsArtificialReef, 2002]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sankAsArtificialReef Context triple: [USS Spiegel Grove, sankAsArtificialReef, 2002]
-
A.
isArtificialReefFor
Indicates that one entity functions as an artificial reef that provides habitat or structural support for another entity, typically marine life or an ecosystem.
-
B.
sunkAs
Indicates that one entity caused another entity to sink or become submerged, typically resulting in its loss or destruction.
-
C.
sankOn
Indicates that one entity moved downward and became submerged or lower in level relative to another entity or reference point.
-
D.
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.
-
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_69d806b4d62c81908d4ced1665414be5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99cfdc9388190af1fdd3cd4717bd8 |
completed | April 11, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69d98f6babd88190a5d529df9584b9a4 |
completed | April 11, 2026, 12:01 a.m. |
| PDg | Predicate description generation | batch_69d99cf93d248190850a7339e0649341 |
completed | April 11, 2026, 12:59 a.m. |
Created at: April 9, 2026, 9:30 p.m.