Triple
T1340135
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rutherford B. Hayes |
E28444
|
entity |
| Predicate | ordinalNumberInOffice |
P2953
|
FINISHED |
| Object | 19 |
—
|
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: 19 | Statement: [Rutherford B. Hayes, ordinalNumberInOffice, 19]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ordinalNumberInOffice Context triple: [Rutherford B. Hayes, ordinalNumberInOffice, 19]
-
A.
ordinalInOffice
chosen
Indicates the numerical order or rank of an individual’s term or tenure in a particular office or position.
-
B.
ordinalNumber
Indicates the position or rank of an entity within an ordered sequence (e.g., first, second, third).
-
C.
officeNumber
Indicates the specific room or suite number assigned to an office within a building or complex.
-
D.
orderInOffice
Indicates that one entity holds a specific sequential position or rank within a defined term or period of holding an office or official role.
-
E.
typeOfOffice
Indicates the specific category or kind of office that an office entity belongs to (e.g., executive, legislative, judicial, or other office types).
- 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_69a49854eb3481908c7d56b2e449a290 |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c21490488190b4281a16c87677d1 |
completed | March 1, 2026, 10:47 p.m. |
| PD | Predicate disambiguation | batch_69a4bef3e8fc8190ac9a1ba9b5879483 |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:56 p.m.