Triple
T6676633
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Breda |
E151867
|
entity |
| Predicate | formerMilitaryRole |
P71690
|
FINISHED |
| Object | fortified city |
—
|
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: fortified city | Statement: [Breda, formerMilitaryRole, fortified city]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: formerMilitaryRole Context triple: [Breda, formerMilitaryRole, fortified city]
-
A.
militaryRole
Indicates the specific function, position, or duty an entity holds within a military organization or context.
-
B.
isMilitaryOfficer
Indicates that the subject holds an official position as an officer within a military organization.
-
C.
servedInRole
Indicates that one entity performed duties or held a position within a specified role or office in relation to another entity.
-
D.
militaryBackground
Indicates that an entity has prior or current experience, service, or training in a military organization.
-
E.
militaryUnitTypeServed
Indicates that an entity served in, or was a member of, a specific type of military unit.
- 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_69c687f830bc81909eb8b04dbb8450b1 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6c0aa8c5c8190a302b261f11b70cb |
completed | March 27, 2026, 5:38 p.m. |
| PD | Predicate disambiguation | batch_69c6ad0b6d00819086205b8ce30dd045 |
completed | March 27, 2026, 4:15 p.m. |
| PDg | Predicate description generation | batch_69c6c0a90a088190978061cb05dbe268 |
completed | March 27, 2026, 5:38 p.m. |
Created at: March 27, 2026, 2:03 p.m.