Triple
T36433569
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charlemagne |
E897507
|
entity |
| Predicate | hasMilitaryEngagement |
P89025
|
FINISHED |
| Object | Battle of Roncevaux Pass |
—
|
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: Battle of Roncevaux Pass | Statement: [Charlemagne, hasMilitaryEngagement, Battle of Roncevaux Pass]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMilitaryEngagement Context triple: [Charlemagne, hasMilitaryEngagement, Battle of Roncevaux Pass]
-
A.
battleInvolvedIn
chosen
Indicates that an entity participated as a combatant or directly took part in a specific battle or military engagement.
-
B.
militaryConflictIn
Indicates that a military conflict takes place within, or is geographically located in, a specified area or region.
-
C.
hasPartOfConflict
Indicates that one conflict includes another conflict as a constituent or subordinate part of it.
-
D.
militaryConflict
Indicates a relationship where two or more parties are engaged in organized, armed hostilities or warfare against each other.
-
E.
hasMonthOfConflict
Indicates that a conflict is associated with, occurs in, or is characterized by a specific calendar month.
- 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_69f76e56636481908eda808ab0273401 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fd4d1854988190be093b103a681798 |
completed | May 8, 2026, 2:40 a.m. |
| PD | Predicate disambiguation | batch_69fd4c8d1a188190897c24527337814a |
completed | May 8, 2026, 2:38 a.m. |
Created at: May 3, 2026, 4:10 p.m.