Triple
T4740587
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Julius Rosenwald |
E105228
|
entity |
| Predicate | numberOfSchoolsFundedEstimate |
P55803
|
FINISHED |
| Object | over 5000 schools, shops, and teacher homes |
—
|
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 5000 schools, shops, and teacher homes | Statement: [Julius Rosenwald, numberOfSchoolsFundedEstimate, over 5000 schools, shops, and teacher homes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfSchoolsFundedEstimate Context triple: [Julius Rosenwald, numberOfSchoolsFundedEstimate, over 5000 schools, shops, and teacher homes]
-
A.
hasNumberOfSchools
chosen
Indicates the quantity of schools associated with a given entity.
-
B.
numberOfUniversities
Indicates the quantity of universities associated with a given entity.
-
C.
hasMajorEducationalInstitutions
Indicates that the subject possesses or hosts significant higher-level educational organizations or facilities, such as universities or major colleges.
-
D.
hasEducationalSupportFrom
Indicates that one entity receives educational assistance, guidance, or resources from another entity.
-
E.
numberOfTargetInstitutions
Indicates the count of institutions that are designated or identified as targets in a given context or dataset.
- 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_69bd43ef87a48190a5bc3600711aa032 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd64a5f3548190a6acf1dcfd64d11d |
completed | March 20, 2026, 3:15 p.m. |
| PD | Predicate disambiguation | batch_69bd6221c3b881908604f35f8de6f16b |
completed | March 20, 2026, 3:05 p.m. |
Created at: March 20, 2026, 1:19 p.m.