Triple
T492554
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | François Mitterrand |
E10220
|
entity |
| Predicate | numberOfTermsAsPresidentOfFrance |
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: [François Mitterrand, numberOfTermsAsPresidentOfFrance, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfTermsAsPresidentOfFrance Context triple: [François Mitterrand, numberOfTermsAsPresidentOfFrance, 2]
-
A.
termCountAsPresident
chosen
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.
presidentSince
Indicates that one entity has held the office of president of another entity starting from a specified point in time.
-
D.
servedAsPresidentDuring
Indicates that a person held the office of president for the duration of a specified time period or event.
-
E.
hasPresident
Indicates that an entity holds the position or role of president for another entity.
- 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_69a2e847df8481909239ec08ccf1e376 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f0faab4881909f65f172198b5bd2 |
completed | Feb. 28, 2026, 1:43 p.m. |
| PD | Predicate disambiguation | batch_69a2edf7ce008190836fb6ab5ea39375 |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.