Triple
T8167255
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carlos Menem |
E190722
|
entity |
| Predicate | presidentialTermCount |
P152
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Carlos Menem, presidentialTermCount, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: presidentialTermCount Context triple: [Carlos Menem, presidentialTermCount, 2]
-
A.
presidentialTerm
Indicates the period of time during which an individual officially serves as president of a country or organization.
-
B.
termCountAsPresident
chosen
Indicates the number of terms an individual has served in the role of president.
-
C.
spansPresidentialTerm
Indicates that an event, period, or entity extends across the duration of a specified presidential term, covering at least part of that term.
-
D.
numberOfTimesInOffice
Indicates the count of separate terms or periods an entity has held a particular office or position.
-
E.
numberOfTermInOffice
Indicates the specific ordinal count of how many terms an entity has served in a particular office or position.
- 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_69ca82c0ef14819083713f4473dd847c |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb46698fb88190a6d520b05cb9b03e |
completed | March 31, 2026, 3:58 a.m. |
| PD | Predicate disambiguation | batch_69cb36a4c40c81909f60aef0e1624c13 |
completed | March 31, 2026, 2:51 a.m. |
Created at: March 30, 2026, 5:39 p.m.