Triple
T5552
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chicago, Illinois, United States |
E108
|
entity |
| Predicate | hasProfessionalField |
P3
|
FINISHED |
| Object | architecture |
—
|
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: architecture | Statement: [Chicago, Illinois, United States, hasProfessionalField, architecture]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProfessionalField Context triple: [Chicago, Illinois, United States, hasProfessionalField, architecture]
-
A.
fieldOfWork
chosen
Indicates the professional or academic domain in which an entity is primarily engaged or specializes.
-
B.
hasFaculty
Indicates that an institution or department possesses or is associated with one or more faculty members.
-
C.
hasAcademicRank
Indicates that an entity holds a specific academic rank or title within an educational or research institution.
-
D.
academicDegree
Indicates that an entity holds or has been awarded a specific academic degree.
-
E.
hasAcademicStaff
Indicates that an institution or organization employs or is associated with one or more academic staff members.
- 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_69a238d6b47881909e68288aed2fd858 |
completed | Feb. 28, 2026, 12:37 a.m. |
| NER | Named-entity recognition | batch_69a23c24b3d08190a714126292fd5479 |
completed | Feb. 28, 2026, 12:51 a.m. |
| PD | Predicate disambiguation | batch_69a23998af288190855f0456740cbd51 |
completed | Feb. 28, 2026, 12:40 a.m. |
Created at: Feb. 28, 2026, 12:40 a.m.