Triple
T6232942
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Huey P. Long |
E139398
|
entity |
| Predicate | intendedOffice |
P9181
|
FINISHED |
| Object | President of the United States (planned 1936 campaign) |
—
|
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: President of the United States (planned 1936 campaign) | Statement: [Huey P. Long, intendedOffice, President of the United States (planned 1936 campaign)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: intendedOffice Context triple: [Huey P. Long, intendedOffice, President of the United States (planned 1936 campaign)]
-
A.
targetedOffice
Indicates that an action, event, or campaign was specifically directed at or focused on a particular office or organizational unit.
-
B.
targetOfficeSought
chosen
Indicates the specific public or organizational office or position that an individual is seeking or running for.
-
C.
includedOffice
Indicates that one office is contained within, or forms part of, another office or organizational unit.
-
D.
specifiesOffice
Indicates that an entity is assigned to or associated with a particular office or official position.
-
E.
hasOffice
Indicates that an entity possesses or maintains an office at a particular location or within a specific organization.
- 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_69c008b0e7ac8190808a59573ee646f3 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c062efa25c8190a54f5a6f5b5ad24f |
completed | March 22, 2026, 9:45 p.m. |
| PD | Predicate disambiguation | batch_69c05601de6481909d0880048fd7b49a |
completed | March 22, 2026, 8:50 p.m. |
Created at: March 22, 2026, 4:22 p.m.