Triple
T512269
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of Michigan |
E10631
|
entity |
| Predicate | cityRole |
P8234
|
FINISHED |
| Object | major economic and cultural anchor of Ann Arbor |
—
|
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: major economic and cultural anchor of Ann Arbor | Statement: [University of Michigan, cityRole, major economic and cultural anchor of Ann Arbor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cityRole Context triple: [University of Michigan, cityRole, major economic and cultural anchor of Ann Arbor]
-
A.
urbanRole
Indicates the function, status, or role that an entity holds within an urban or city context.
-
B.
hasCityRole
chosen
Indicates that an entity holds or is assigned a specific role, function, or status within a particular city.
-
C.
hasUrbanRole
Indicates that an entity plays a specific functional or social role within an urban or city context.
-
D.
populationRole
Indicates the role or function that an entity has within a population or demographic context.
-
E.
deFactoRole
Indicates that an entity effectively functions in a role or capacity in practice, even if that role is not formally or officially assigned.
- 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_69a2e84a0d08819087e01863fcd9abf1 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f16768c081909d05537ff070868b |
completed | Feb. 28, 2026, 1:45 p.m. |
| PD | Predicate disambiguation | batch_69a2edff001c81909182a7e26c6dc51b |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.