Triple
T24119171
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Francisco de Bobadilla |
E597602
|
entity |
| Predicate | shipwreckContext |
P154925
|
FINISHED |
| Object | return voyage to Spain |
—
|
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: return voyage to Spain | Statement: [Francisco de Bobadilla, shipwreckContext, return voyage to Spain]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: shipwreckContext Context triple: [Francisco de Bobadilla, shipwreckContext, return voyage to Spain]
-
A.
shipwreckUse
Indicates that an entity makes use of, interacts with, or derives benefit from a shipwreck.
-
B.
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.
-
C.
shipwreck
Indicates that a vessel has been destroyed, stranded, or severely damaged, typically at sea or near a shoreline.
-
D.
shipwreckRoute
Indicates a route or path along which a ship traveled or was intended to travel that ultimately resulted in a shipwreck at some point on that route.
-
E.
shipwreckType
Indicates the specific kind or classification of a shipwreck associated with an entity.
- 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_69e288c74200819098ab875b592cb39f |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1dee1a2608190870ade02495c5ebe |
completed | April 29, 2026, 10:35 a.m. |
| PD | Predicate disambiguation | batch_69f1765650fc8190a6bc1eb512b240bf |
completed | April 29, 2026, 3:09 a.m. |
| PDg | Predicate description generation | batch_69f17c28b684819084eea522126463f8 |
completed | April 29, 2026, 3:34 a.m. |
Created at: April 17, 2026, 11:05 p.m.