Triple
T2047622
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Helmholtz Association |
E45489
|
entity |
| Predicate | numberOfResearchCentres |
P35551
|
FINISHED |
| Object | 18 |
—
|
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: 18 | Statement: [Helmholtz Association, numberOfResearchCentres, 18]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfResearchCentres Context triple: [Helmholtz Association, numberOfResearchCentres, 18]
-
A.
hasResearchCentersIn
Indicates that an entity maintains or operates research centers located within a specified place or region.
-
B.
researchCenter
Indicates that one entity functions as a research center associated with, operated by, or focused on the other entity.
-
C.
numberOfUniversities
Indicates the quantity of universities associated with a given entity.
-
D.
numberOfTargetInstitutions
Indicates the count of institutions that are designated or identified as targets in a given context or dataset.
-
E.
numberOfCampuses
Indicates the total count of campuses associated with a given entity.
- 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_69a8891948208190ab7898da21824c77 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb974e8488190887b840c2cb88b3a |
completed | March 7, 2026, 5:36 a.m. |
| PD | Predicate disambiguation | batch_69abb7abba508190b872f345d3ba51bb |
completed | March 7, 2026, 5:29 a.m. |
| PDg | Predicate description generation | batch_69abb94ec400819097596732aabed854 |
completed | March 7, 2026, 5:36 a.m. |
Created at: March 4, 2026, 7:39 p.m.