Triple
T26770562
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | West Berlin Police |
E675064
|
entity |
| Predicate | hadTypeOf |
P16808
|
FINISHED |
| Object | civilian police organization |
—
|
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: civilian police organization | Statement: [West Berlin Police, hadTypeOf, civilian police organization]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadTypeOf Context triple: [West Berlin Police, hadTypeOf, civilian police organization]
-
A.
haveType
chosen
Indicates that an entity belongs to or is classified under a specified type or category.
-
B.
hadModel
Indicates that an entity possessed, used, or was associated with a particular model (e.g., a product, design, or version) at some point in time.
-
C.
hadOrgan
Indicates that an entity previously possessed or contained a specific organ as part of its body.
-
D.
hadCustom
Indicates that an entity previously possessed or was associated with a customized or user-defined version of something.
-
E.
hadClass
Indicates that an entity attended or participated in a particular class or course.
- 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_69eecda85298819097ee1c38a3d772e7 |
completed | April 27, 2026, 2:44 a.m. |
| NER | Named-entity recognition | batch_69f6192c45fc819094e7dd70fd9cc333 |
completed | May 2, 2026, 3:33 p.m. |
| PD | Predicate disambiguation | batch_69f60b8dfa0c8190864e1a940024d0a0 |
completed | May 2, 2026, 2:34 p.m. |
Created at: April 27, 2026, 4:02 a.m.