Triple
T35959369
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SS Regina wreck (1913 storm) |
E1039953
|
entity |
| Predicate | hasInterestFor |
P66206
|
FINISHED |
| Object | maritime historians |
—
|
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: maritime historians | Statement: [SS Regina wreck (1913 storm), hasInterestFor, maritime historians]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasInterestFor Context triple: [SS Regina wreck (1913 storm), hasInterestFor, maritime historians]
-
A.
hasInterestType
Indicates that an entity is associated with a specific category or type of interest it holds or is concerned with.
-
B.
hasHumanInterest
Indicates that something is of particular relevance, appeal, or concern to people, often engaging their emotions, curiosity, or personal experiences.
-
C.
hasPolicyInterestOf
Indicates that one entity holds or is associated with a specific policy interest belonging to or concerning another entity.
-
D.
subjectInterest
chosen
Indicates that the subject has an interest in, or is concerned with, the object.
-
E.
interestMayBe
Indicates that one entity potentially has an interest in, or may be interested in, another entity or subject.
- 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_6a008c77d4dc8190b342d7407eaf48fa |
completed | May 10, 2026, 1:47 p.m. |
| PD | Predicate disambiguation | batch_6a008c18531c8190bbe883b73e6d023f |
completed | May 10, 2026, 1:46 p.m. |
Created at: May 3, 2026, 4:07 p.m.