Triple
T4055213
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maarten Tromp |
E84676
|
entity |
| Predicate | hasPartInFiction |
P31994
|
FINISHED |
| Object | portrayals in Dutch naval literature |
—
|
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: portrayals in Dutch naval literature | Statement: [Maarten Tromp, hasPartInFiction, portrayals in Dutch naval literature]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPartInFiction Context triple: [Maarten Tromp, hasPartInFiction, portrayals in Dutch naval literature]
-
A.
hasFictionComponent
chosen
Indicates that something includes, contains, or is composed in part of a fictional element or work.
-
B.
hasFictionalForm
Indicates that an entity has a counterpart or representation that exists within a fictional or imaginary context.
-
C.
fictionalOrigin
Indicates that one entity originates from, or was first introduced within, a fictional work, universe, or narrative created by another entity.
-
D.
literaryWorkInStory
Indicates that one literary work is referenced, featured, or embedded within the narrative of another story.
-
E.
fictionalMedium
Indicates that a work of fiction is presented or conveyed through a particular medium or format (such as a book, film, game, or comic).
- 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_69aed933bec881909edfa28ebb69c634 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefbabec608190a35d39d3d03b928b |
completed | March 9, 2026, 4:56 p.m. |
| PD | Predicate disambiguation | batch_69aef90249e4819095e9e043bc4aa9a6 |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:38 p.m.