Triple
T36666547
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frank Savage |
E905279
|
entity |
| Predicate | fictionalTheaterOfWar |
P199429
|
FINISHED |
| Object | European Theater of Operations |
—
|
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: European Theater of Operations | Statement: [Frank Savage, fictionalTheaterOfWar, European Theater of Operations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalTheaterOfWar Context triple: [Frank Savage, fictionalTheaterOfWar, European Theater of Operations]
-
A.
theaterOfWar
Indicates that a specified location or region is the primary area where a particular conflict, war, or military operation takes place.
-
B.
militaryTheater
Indicates that an entity is a geographic or operational area where military operations or campaigns are conducted.
-
C.
fictionalConflict
Indicates a relationship where one fictional entity is in opposition, dispute, or struggle with another within a narrative context.
-
D.
hasFictionalWar
Indicates that there exists a fictional or imagined war involving the related entities.
-
E.
fictionalFocus
Indicates that the primary emphasis or attention within a context is placed on fictional content, elements, or aspects.
- 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_69f76e6f10008190aea41746aa1b186e |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69ff38b960808190a8263348f1e5c0e4 |
completed | May 9, 2026, 1:38 p.m. |
| PD | Predicate disambiguation | batch_69ff37d97d9c8190849b2bac14f9af1d |
completed | May 9, 2026, 1:34 p.m. |
| PDg | Predicate description generation | batch_69ff38b8843c819097359a4d77e9d442 |
completed | May 9, 2026, 1:38 p.m. |
Created at: May 3, 2026, 4:12 p.m.