Triple
T35959079
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SS Bannockburn wreck |
E1039946
|
entity |
| Predicate | partOfMaritimeHistoryOf |
P186498
|
FINISHED |
| Object | Great Lakes |
—
|
NE NERFINISHED |
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: Great Lakes | Statement: [SS Bannockburn wreck, partOfMaritimeHistoryOf, Great Lakes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: partOfMaritimeHistoryOf Context triple: [SS Bannockburn wreck, partOfMaritimeHistoryOf, Great Lakes]
-
A.
hasMaritimeHeritage
Indicates that an entity possesses a historical, cultural, or traditional connection to maritime activities, seafaring, or the sea.
-
B.
hasShipwrecksFrom
chosen
Indicates that something contains or is associated with shipwrecks originating from a specified source, location, or time period.
-
C.
hasMaritimeMuseum
Indicates that one entity possesses, hosts, or includes a maritime museum as part of its features or facilities.
-
D.
sharesMaritimeHeritageWith
Indicates that two entities have a common maritime history, traditions, or seafaring cultural background.
-
E.
navalHistory
Indicates a relationship where an entity is associated with the study, record, or events of naval or maritime military history.
- 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_69f76e26b21081909fd9ffb3aff6c77a |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a00b6199c348190b1e90375f737d026 |
completed | May 10, 2026, 4:45 p.m. |
| PD | Predicate disambiguation | batch_6a00b5169a7881909a07167c4188731f |
completed | May 10, 2026, 4:40 p.m. |
Created at: May 3, 2026, 4:07 p.m.