Triple
T17204557
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zine El Abidine Ben Ali |
E417565
|
entity |
| Predicate | termLengthInYears |
P33746
|
FINISHED |
| Object | about 23.2 |
—
|
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: about 23.2 | Statement: [Zine El Abidine Ben Ali, termLengthInYears, about 23.2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: termLengthInYears Context triple: [Zine El Abidine Ben Ali, termLengthInYears, about 23.2]
-
A.
termLength
Indicates the duration or period of time for which an agreement, position, or condition remains in effect.
-
B.
termLengthNumber
chosen
Indicates the numerical value representing the duration or length of a specified term.
-
C.
electsTermLength
Indicates the length of time for which an entity is elected to hold a particular position or office.
-
D.
numberOfTermInOffice
Indicates the specific ordinal count of how many terms an entity has served in a particular office or position.
-
E.
legislativeTermType
Indicates the specific category or classification of a legislative term within a legislative body or system.
- 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_69d886d6ba8c819093215917b3d01689 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42db1e01c81909db0491fd9f49bed |
completed | April 19, 2026, 1:19 a.m. |
| PD | Predicate disambiguation | batch_69e3831e354881908c5505ffd15c84e9 |
completed | April 18, 2026, 1:11 p.m. |
Created at: April 10, 2026, 5:38 a.m.