Triple
T35959239
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SS Vienna wreck |
E1039950
|
entity |
| Predicate | sankInEra |
P184476
|
FINISHED |
| Object | age of wooden steamships |
—
|
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: age of wooden steamships | Statement: [SS Vienna wreck, sankInEra, age of wooden steamships]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sankInEra Context triple: [SS Vienna wreck, sankInEra, age of wooden steamships]
-
A.
sankInCentury
Indicates that an entity (typically a ship or vessel) sank during a specified century.
-
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.
shipSankIn
Indicates that a specific ship sank (was lost or submerged) in a particular location or body of water.
-
D.
sunkBy
Indicates that one entity (typically a vessel or structure) was caused to sink or be destroyed in water by another entity.
-
E.
yearOfSinking
Indicates the specific calendar year in which an entity (typically a vessel or structure) sank.
- 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_69f76e26b21081909fd9ffb3aff6c77a |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7b35e32d481909ef0220e6f6ff4a8 |
completed | May 3, 2026, 8:43 p.m. |
| PD | Predicate disambiguation | batch_69f7b1bad2e88190963ab4ee5d4f2038 |
completed | May 3, 2026, 8:36 p.m. |
| PDg | Predicate description generation | batch_69f7b2c66054819083897e25edb65ba7 |
completed | May 3, 2026, 8:40 p.m. |
Created at: May 3, 2026, 4:07 p.m.