Triple
T33657721
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arthur administration |
E862267
|
entity |
| Predicate | termNumberOfPresident |
P177481
|
FINISHED |
| Object | 21 |
—
|
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: 21 | Statement: [Arthur administration, termNumberOfPresident, 21]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: termNumberOfPresident Context triple: [Arthur administration, termNumberOfPresident, 21]
-
A.
termCountAsPresident
Indicates the number of terms an individual has served in the role of president.
-
B.
presidentialTerm
Indicates the period of time during which an individual officially serves as president of a country or organization.
-
C.
presidentNumberElected
Indicates the ordinal number corresponding to the sequence in which a person was elected president.
-
D.
incumbentPresidentNumber
Indicates the ordinal position or sequence number of the person currently serving as president.
-
E.
presidentSince
Indicates that one entity has held the office of president of another entity starting from a specified point in time.
- 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_69f349840ba881908e3bfce536aeb92b |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6fb93224881908fc66fe76115fcdb |
completed | May 3, 2026, 7:38 a.m. |
| PD | Predicate disambiguation | batch_69f6f96badb08190994442c2aba840b1 |
completed | May 3, 2026, 7:29 a.m. |
| PDg | Predicate description generation | batch_69f6fb17d5ec81909091e37e1ddbe577 |
completed | May 3, 2026, 7:36 a.m. |
Created at: May 1, 2026, 1:42 a.m.