Triple
T14383088
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kellstadt Graduate School of Business |
E356651
|
entity |
| Predicate | locatedInTheDistrict |
P40
|
FINISHED |
| Object | downtown Chicago |
—
|
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: downtown Chicago | Statement: [Kellstadt Graduate School of Business, locatedInTheDistrict, downtown Chicago]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: locatedInTheDistrict Context triple: [Kellstadt Graduate School of Business, locatedInTheDistrict, downtown Chicago]
-
A.
basedInDistrict
Indicates that an entity is located or has its primary base of operations within a specific administrative district.
-
B.
locatedInRegionalDistrict
Indicates that one entity is geographically situated within the boundaries of a specified regional district.
-
C.
locatedIn
chosen
Indicates that one entity exists or is situated within the spatial, administrative, or conceptual boundaries of another entity.
-
D.
locatedNearGovernmentDistrict
Indicates that one entity is situated close to or in the immediate vicinity of a government district.
-
E.
appliedInDistrict
Indicates that an application or action was submitted or carried out within a specific administrative district.
- 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_69d827927c988190ad98bb0360981783 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de900d28c88190a37feee4743563de |
completed | April 14, 2026, 7:05 p.m. |
| PD | Predicate disambiguation | batch_69de2aa024c48190805df6a9d63deb10 |
completed | April 14, 2026, 11:53 a.m. |
Created at: April 10, 2026, 1:16 a.m.