Triple
T27055655
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maria Pappas |
E684889
|
entity |
| Predicate | hasPublicOfficeType |
P5164
|
FINISHED |
| Object | county-level office |
—
|
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: county-level office | Statement: [Maria Pappas, hasPublicOfficeType, county-level office]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPublicOfficeType Context triple: [Maria Pappas, hasPublicOfficeType, county-level office]
-
A.
hasOfficeHolderType
Indicates that an office or position is associated with a specific type or category of office holder (e.g., elected official, appointed official).
-
B.
hasOfficeType
chosen
Indicates that an entity’s office is classified as a specific type or category of office.
-
C.
areaOfPublicOffice
Indicates the geographic or jurisdictional area over which a public office or official has authority or responsibility.
-
D.
hasOffice
Indicates that an entity possesses or maintains an office at a particular location or within a specific organization.
-
E.
typeOfOffice
Indicates the specific category or kind of office that an office entity belongs to (e.g., executive, legislative, judicial, or other office types).
- 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_69ef14829fac8190914bef9ecc3005d7 |
completed | April 27, 2026, 7:47 a.m. |
| NER | Named-entity recognition | batch_69f6691f5e188190b12c7b2eb729a45e |
completed | May 2, 2026, 9:14 p.m. |
| PD | Predicate disambiguation | batch_69f66598d6008190a7ca8ff80399fd34 |
completed | May 2, 2026, 8:59 p.m. |
Created at: April 27, 2026, 8:17 a.m.