Triple
T263452
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fireside chats on banking crisis |
E5801
|
entity |
| Predicate | hasPresidentNumber |
P2349
|
FINISHED |
| Object | 32nd 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: 32nd President of the United States | Statement: [Fireside chats on banking crisis, hasPresidentNumber, 32nd President of the United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPresidentNumber Context triple: [Fireside chats on banking crisis, hasPresidentNumber, 32nd President of the United States]
-
A.
presidentialNumber
chosen
Indicates the ordinal position a person holds in a sequence of presidents (e.g., first, second, third president).
-
B.
hasPresident
Indicates that an entity holds the position or role of president for another entity.
-
C.
termCountAsPresident
Indicates the number of terms an individual has served in the role of president.
-
D.
presidentElect
Indicates that one entity has been elected to be president of another entity (such as a country or organization) but has not yet assumed the office.
-
E.
supportedByPresident
Indicates that an action, proposal, or entity receives endorsement or backing from the president.
- 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_69a258dd8ea08190ac554a1cc8dfd8c3 |
completed | Feb. 28, 2026, 2:54 a.m. |
| NER | Named-entity recognition | batch_69a25d8e809881908a58c9a4e3ba07c3 |
completed | Feb. 28, 2026, 3:14 a.m. |
| PD | Predicate disambiguation | batch_69a25b6e07748190834022a65ba6d803 |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:55 a.m.