Triple
T33904785
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nurse Diana Murdoch |
E869151
|
entity |
| Predicate | characterInConflict |
P199752
|
FINISHED |
| Object | North African campaign |
—
|
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: North African campaign | Statement: [Nurse Diana Murdoch, characterInConflict, North African campaign]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterInConflict Context triple: [Nurse Diana Murdoch, characterInConflict, North African campaign]
-
A.
character1
Indicates that the subject is identified as the first or primary character in a narrative or context.
-
B.
protagonistConfronts
Indicates that a main character directly faces and challenges another character, force, or problem in a conflictual encounter.
-
C.
storyConflict
Indicates a relationship where a story contains or centers around a central problem, opposition, or tension that drives its plot.
-
D.
contestedByFictionalCharacter
Indicates that a fictional character challenges, disputes, or opposes something, such as a claim, decision, or situation.
-
E.
helpsCharacterConfront
Indicates that one character actively supports or enables another character in facing and dealing with a difficult issue, fear, or challenge.
- 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_69f34997703c8190866b1d404bce531f |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69ff53389a0481908b2baeb43c6294f0 |
completed | May 9, 2026, 3:31 p.m. |
| PD | Predicate disambiguation | batch_69ff52e2b4b88190b38d160d771fe14b |
completed | May 9, 2026, 3:29 p.m. |
| PDg | Predicate description generation | batch_69ff5337e6f88190ae0418335477063c |
completed | May 9, 2026, 3:31 p.m. |
Created at: May 1, 2026, 1:48 a.m.