Triple
T3019553
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | College of Cities |
E82420
|
entity |
| Predicate | primaryInterest |
P44657
|
FINISHED |
| Object | urban privileges |
—
|
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: urban privileges | Statement: [College of Cities, primaryInterest, urban privileges]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryInterest Context triple: [College of Cities, primaryInterest, urban privileges]
-
A.
primaryArea
Indicates that one entity is the main or most important area, domain, or field associated with another entity.
-
B.
primaryTopicOf
Indicates that a given subject is the main or central topic described by another resource (such as a document, page, or record).
-
C.
primaryWork
Indicates that one work is the main or most significant work associated with a given entity, as opposed to other secondary or related works.
-
D.
primaryDedication
Indicates the main person, concept, or entity to which something (such as a work, structure, or event) is formally dedicated above all others.
-
E.
primaryTarget
Indicates that an entity is the main or most important target of another entity’s action, focus, or effect.
- 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_69ad8b1fb34081908c1b873e2b7273e1 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9a940c048190bc46e2c8001db8c0 |
completed | March 8, 2026, 3:49 p.m. |
| PD | Predicate disambiguation | batch_69ad961c430c8190ac48f2e3c7e7c649 |
completed | March 8, 2026, 3:30 p.m. |
| PDg | Predicate description generation | batch_69ad97f6af3881909f4547967384114c |
completed | March 8, 2026, 3:38 p.m. |
Created at: March 8, 2026, 3 p.m.