Triple
T27247246
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clement Calhoun Young |
E687376
|
entity |
| Predicate | lieutenantGovernorDuringTerm |
P162276
|
FINISHED |
| Object | Burton M. Green |
—
|
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: Burton M. Green | Statement: [Clement Calhoun Young, lieutenantGovernorDuringTerm, Burton M. Green]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lieutenantGovernorDuringTerm Context triple: [Clement Calhoun Young, lieutenantGovernorDuringTerm, Burton M. Green]
-
A.
servedAsGovernorUntil
Indicates that an entity held the position of governor up to a specified end date or time.
-
B.
governorTerm
Indicates the time period during which a person holds or held the office of governor of a specific jurisdiction.
-
C.
builtDuringGovernorshipOf
Indicates that the construction of one entity occurred during the period when another entity held a governing office.
-
D.
lieutenantGovernorElected
Indicates that an individual attains the position of lieutenant governor through an electoral process.
-
E.
laterGovernor
Indicates that one entity subsequently became the governor of a place or jurisdiction associated with 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_69ef355547408190b5ca0d777c65040a |
completed | April 27, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69f626b2ba8c819090a9eb67cf9cb701 |
completed | May 2, 2026, 4:30 p.m. |
| PD | Predicate disambiguation | batch_69f620e38aec8190bb184edcdbd6da64 |
completed | May 2, 2026, 4:05 p.m. |
| PDg | Predicate description generation | batch_69f622a8fe7c819096e8a43db263a423 |
completed | May 2, 2026, 4:13 p.m. |
Created at: April 27, 2026, 10:42 a.m.