Triple
T22596749
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blue at the Mizzen |
E574700
|
entity |
| Predicate | hasCompanionCharacterProfession |
P148872
|
FINISHED |
| Object | naval surgeon |
—
|
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: naval surgeon | Statement: [Blue at the Mizzen, hasCompanionCharacterProfession, naval surgeon]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCompanionCharacterProfession Context triple: [Blue at the Mizzen, hasCompanionCharacterProfession, naval surgeon]
-
A.
hasCompanionRoleIn
Indicates that an entity serves in a companion or supporting role within a specified context, activity, or relationship.
-
B.
hasProfessionTrait
Indicates that an entity possesses a particular characteristic, quality, or attribute specifically related to their profession or occupational role.
-
C.
hasMagicCompanion
Indicates that an entity is accompanied by or associated with another entity that serves as its magical companion.
-
D.
hasCompanionPiece
Indicates that one item is conceptually or functionally paired with another item as its companion piece.
-
E.
hasCoProtagonistOccupation
Indicates that two or more co-protagonists share a specified occupation or professional role.
- 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_69e245bc11308190b69d794d5d1e0bb6 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f16269a56881909bb5af0258150f93 |
completed | April 29, 2026, 1:44 a.m. |
| PD | Predicate disambiguation | batch_69ee627be4248190889a88764624e174 |
completed | April 26, 2026, 7:07 p.m. |
| PDg | Predicate description generation | batch_69ee8841e9cc81908d23b34215e3be71 |
completed | April 26, 2026, 9:48 p.m. |
Created at: April 17, 2026, 2:50 p.m.