Triple
T34275242
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elizabeth Ruth Grable |
E879433
|
entity |
| Predicate | militaryConflictAssociatedWith |
P13112
|
FINISHED |
| Object | World War II |
—
|
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: World War II | Statement: [Elizabeth Ruth Grable, militaryConflictAssociatedWith, World War II]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: militaryConflictAssociatedWith Context triple: [Elizabeth Ruth Grable, militaryConflictAssociatedWith, World War II]
-
A.
militaryConflictIn
Indicates that a military conflict takes place within, or is geographically located in, a specified area or region.
-
B.
militaryConflict
Indicates a relationship where two or more parties are engaged in organized, armed hostilities or warfare against each other.
-
C.
partOfHistoricalConflict
Indicates that one entity participated in, belonged to, or was involved as a component of a larger historical conflict or war.
-
D.
warParticipatedIn
chosen
Indicates that an entity took part as a combatant or active participant in a specific war or armed conflict.
-
E.
militaryEventMentionedIn
Indicates that a specific military event is referenced or discussed within a particular document, text, 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_69f349b5f6648190b9420d94a4cd16e0 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69fcc7338120819081cb46547d60f2cb |
completed | May 7, 2026, 5:09 p.m. |
| PD | Predicate disambiguation | batch_69fcc58566a0819082d5ea36e03bf0c6 |
completed | May 7, 2026, 5:01 p.m. |
Created at: May 1, 2026, 1:56 a.m.