Triple
T13729603
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Barbavera |
E329759
|
entity |
| Predicate | roleInBattleOfSluys |
P111334
|
FINISHED |
| Object | commander of Genoese forces |
—
|
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: commander of Genoese forces | Statement: [Barbavera, roleInBattleOfSluys, commander of Genoese forces]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleInBattleOfSluys Context triple: [Barbavera, roleInBattleOfSluys, commander of Genoese forces]
-
A.
roleAtBattleOfMorlaix
Indicates the specific role, position, or function an entity held during the Battle of Morlaix.
-
B.
roleInFirstFleet
Indicates that an entity held a specific role or position within the First Fleet.
-
C.
roleInBattleOfSalamis
Indicates the specific role or involvement an entity had in the Battle of Salamis.
-
D.
roleInWarsOfTheRoses
Indicates the specific part or function an entity played in the historical conflict known as the Wars of the Roses.
-
E.
roleAtBattleOfToulon
Indicates the specific role, position, or function an entity held during the Battle of Toulon.
- 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_69d80772315881908f980cae40d91664 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69de01f746cc8190abde237bbb7e6c78 |
completed | April 14, 2026, 8:59 a.m. |
| PD | Predicate disambiguation | batch_69dbbe950b148190ba0df8a749269ec6 |
completed | April 12, 2026, 3:47 p.m. |
| PDg | Predicate description generation | batch_69dbc59ca1a88190a6abd3bd00554c93 |
completed | April 12, 2026, 4:17 p.m. |
Created at: April 9, 2026, 9:55 p.m.