Triple
T26748215
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RMS Lancastria |
E674459
|
entity |
| Predicate | maritimeDisasterType |
P129444
|
FINISHED |
| Object | wartime sinking |
—
|
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: wartime sinking | Statement: [RMS Lancastria, maritimeDisasterType, wartime sinking]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maritimeDisasterType Context triple: [RMS Lancastria, maritimeDisasterType, wartime sinking]
-
A.
shipwreckType
chosen
Indicates the specific kind or classification of a shipwreck associated with an entity.
-
B.
oneOfDeadliestMaritimeDisastersIn
Indicates that an event is among the most deadly maritime disasters that occurred within a specified place or time period.
-
C.
shipwreckEvent
Indicates an event in which a ship is destroyed, stranded, or severely damaged, typically resulting in loss or abandonment at sea or near a shoreline.
-
D.
shipwreck
Indicates that a vessel has been destroyed, stranded, or severely damaged, typically at sea or near a shoreline.
-
E.
shipTypeInvolved
Indicates that a particular type or class of ship is involved or participates in a specified event, situation, or relationship.
- 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_69eecda63a3881908095c47900692e65 |
completed | April 27, 2026, 2:44 a.m. |
| NER | Named-entity recognition | batch_69f6383625cc8190aa223d8ef655743c |
completed | May 2, 2026, 5:45 p.m. |
| PD | Predicate disambiguation | batch_69f63709e4848190b5cf322e06b23fb6 |
completed | May 2, 2026, 5:40 p.m. |
Created at: April 27, 2026, 3:52 a.m.