Triple
T6808478
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lyndon B. Johnson Presidential Library |
E156369
|
entity |
| Predicate | focusesOnOffice |
P31
|
FINISHED |
| Object | 36th President of the United States |
—
|
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: 36th President of the United States | Statement: [Lyndon B. Johnson Presidential Library, focusesOnOffice, 36th President of the United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: focusesOnOffice Context triple: [Lyndon B. Johnson Presidential Library, focusesOnOffice, 36th President of the United States]
-
A.
focusesOn
chosen
Indicates that one entity directs its attention, effort, or primary activity toward another entity or specific subject.
-
B.
officeIsIn
Indicates that one office is located within or inside another specified place or building.
-
C.
includedOffice
Indicates that one office is contained within, or forms part of, another office or organizational unit.
-
D.
targetedOffice
Indicates that an action, event, or campaign was specifically directed at or focused on a particular office or organizational unit.
-
E.
focusesOnWork
Indicates that an entity directs its attention, effort, or primary activity toward work-related tasks or responsibilities.
- 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_69c68826e6a48190a3d220b541e639de |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d30b56c48190a5b244ea7e0c669a |
completed | March 27, 2026, 6:57 p.m. |
| PD | Predicate disambiguation | batch_69c6d099bf08819089a9f9894d037e74 |
completed | March 27, 2026, 6:46 p.m. |
Created at: March 27, 2026, 2:16 p.m.