Triple
T19244090
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Adrienne Adams |
E481202
|
entity |
| Predicate | office held level |
P135036
|
FINISHED |
| Object | municipal |
—
|
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: municipal | Statement: [Adrienne Adams, office held level, municipal]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: office held level Context triple: [Adrienne Adams, office held level, municipal]
-
A.
officeFounded
Indicates that an office or branch of an organization was established or created at a particular time or place.
-
B.
officeUnder
Indicates that one office is subordinate to, managed by, or organizationally within the authority of another office.
-
C.
officeHeldOf
Indicates that a specific office or position is (or was) held by a particular person or entity.
-
D.
officeIsIn
Indicates that one office is located within or inside another specified place or building.
-
E.
aboutOfficeHeld
Indicates that one entity is related to, or provides information about, a specific office or position that is or was held by another entity.
- 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_69d8e8cd9d1081908a181d02b88b59b8 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5faf47820819081e8b6af852bb1dd |
completed | April 20, 2026, 10:07 a.m. |
| PD | Predicate disambiguation | batch_69e4dd002d00819088b625056edfb74e |
completed | April 19, 2026, 1:47 p.m. |
| PDg | Predicate description generation | batch_69e4ddcf50108190a09d0f1291c17374 |
completed | April 19, 2026, 1:51 p.m. |
Created at: April 10, 2026, 1:27 p.m.