Triple
T25914265
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guava Island (fictional Caribbean island) |
E652985
|
entity |
| Predicate | hasFictionalRulerOrPower |
P67496
|
FINISHED |
| Object | Red Cargo |
—
|
NE NERFINISHED |
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: Red Cargo | Statement: [Guava Island (fictional Caribbean island), hasFictionalRulerOrPower, Red Cargo]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalRulerOrPower Context triple: [Guava Island (fictional Caribbean island), hasFictionalRulerOrPower, Red Cargo]
-
A.
hasFictionalLeader
chosen
Indicates that an entity is led or governed by a leader who is a fictional character rather than a real person.
-
B.
appearsInFictionalMonarchy
Indicates that an entity is featured as part of a fictional monarchy within a narrative work.
-
C.
hasFictionalBackstory
Indicates that an entity is associated with an invented or imaginary narrative background rather than a real-world history.
-
D.
hasFamousRuler
Indicates that an entity is or was ruled by a ruler who is widely recognized or historically notable.
-
E.
isFictionalPersonFrom
Indicates that a fictional person originates from or is associated with a particular place or source.
- 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_69e7ab3e025c819086771607157f0015 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f707f7959881908f037f0d6b1d0c36 |
completed | May 3, 2026, 8:31 a.m. |
| PD | Predicate disambiguation | batch_69f700fc274c8190a128593dc7c7abd0 |
completed | May 3, 2026, 8:02 a.m. |
Created at: April 22, 2026, 8:30 a.m.