Triple
T3628683
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dark Energy Survey |
E76900
|
entity |
| Predicate | numberOfInstitutions |
P276
|
FINISHED |
| Object | over 25 institutions |
—
|
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: over 25 institutions | Statement: [Dark Energy Survey, numberOfInstitutions, over 25 institutions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfInstitutions Context triple: [Dark Energy Survey, numberOfInstitutions, over 25 institutions]
-
A.
numberOfTargetInstitutions
Indicates the count of institutions that are designated or identified as targets in a given context or dataset.
-
B.
hasNumberOfMemberInstitutions
chosen
Indicates the quantitative count of member institutions associated with a given entity.
-
C.
numberOfUniversities
Indicates the quantity of universities associated with a given entity.
-
D.
numberOfCampuses
Indicates the total count of campuses associated with a given entity.
-
E.
establishedInstitution
Indicates that an entity founded, created, or formally set up an institution or organization.
- 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_69ad85dc03948190b35b7189e4175bcc |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc2df2b708190afef6925a53ec551 |
completed | March 8, 2026, 6:41 p.m. |
| PD | Predicate disambiguation | batch_69adb8410a5881909c94818d7060b2b0 |
completed | March 8, 2026, 5:56 p.m. |
Created at: March 8, 2026, 3:23 p.m.