Triple
T35466932
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Antonio Delgado |
E1025099
|
entity |
| Predicate | order in office |
P351
|
FINISHED |
| Object | Lieutenant Governor of New York |
—
|
NE NERFINISHED |
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: Lieutenant Governor of New York | Statement: [Antonio Delgado, order in office, Lieutenant Governor of New York]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: order in office Context triple: [Antonio Delgado, order in office, Lieutenant Governor of New York]
-
A.
orderInOffice
chosen
Indicates that one entity holds a specific sequential position or rank within a defined term or period of holding an office or official role.
-
B.
officeUnder
Indicates that one office is subordinate to, managed by, or organizationally within the authority of another office.
-
C.
layOffice
Indicates that one entity holds or occupies a particular office, position, or role in relation to another entity.
-
D.
officeState
Indicates the current operational or functional status of an office (e.g., open, closed, active, inactive) at a given time.
-
E.
officeIs
Indicates that one entity serves as the office or official workplace location of another entity.
- 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_69f76dfa20d0819089585dc2cf653aea |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7a225a77c81908f8953ccfeb14336 |
completed | May 3, 2026, 7:29 p.m. |
| PD | Predicate disambiguation | batch_69f7a06d4f108190bae3ab9ae431d2c7 |
completed | May 3, 2026, 7:22 p.m. |
Created at: May 3, 2026, 4:04 p.m.