Triple
T1779414
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Berlin-Brandenburg metropolitan region |
E39254
|
entity |
| Predicate | hasMajorSector |
P16009
|
FINISHED |
| Object | research and development |
—
|
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: research and development | Statement: [Berlin-Brandenburg metropolitan region, hasMajorSector, research and development]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMajorSector Context triple: [Berlin-Brandenburg metropolitan region, hasMajorSector, research and development]
-
A.
hasMajorCategory
Indicates that something is associated with or classified under a primary, overarching category.
-
B.
hasMajorOrganization
Indicates that an entity is associated with or primarily represented by a major organization.
-
C.
hasMajorEmployer
Indicates that an entity has a primary or most significant employer with which it is chiefly affiliated for work or occupation.
-
D.
hasMajorBusinessLine
chosen
Indicates that an entity conducts a primary or significant line of business in a specified area, sector, or activity.
-
E.
hasMajorMarket
Indicates that an entity has a primary or most significant market in a specified location or segment.
- 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_69a88630519c8190a17addd83c4a3ef4 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab74dc9d1481908084ef07872a71f8 |
completed | March 7, 2026, 12:44 a.m. |
| PD | Predicate disambiguation | batch_69aa61cf3ca881908641fd73ce2f7c9d |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:31 p.m.