Triple
T1713850
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MR |
E37244
|
entity |
| Predicate | isFrancophoneParty |
P32253
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [MR, isFrancophoneParty, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isFrancophoneParty Context triple: [MR, isFrancophoneParty, true]
-
A.
hasFrenchSector
Indicates that an entity includes, controls, or is associated with a sector or area designated as French.
-
B.
FrenchRole
Indicates a role or position that an entity holds specifically within a French context (e.g., in France or related to French institutions, culture, or language).
-
C.
isLinguaFrancaOf
Indicates that a language serves as a common medium of communication between speakers of different native languages within a particular region, community, or context.
-
D.
FrenchObjective
Indicates that an entity serves as the goal, target, or object of an action or relation specifically within a French linguistic or contextual framework.
-
E.
FrenchSupport
Indicates that one entity provides support, assistance, or backing to another in a specifically French context (e.g., by French actors, in France, or involving the French language or institutions).
- 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_69a8861912dc8190931af43b4b9158a7 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69ab7521878c8190b9e7739b8c3fc705 |
completed | March 7, 2026, 12:45 a.m. |
| PD | Predicate disambiguation | batch_69aa61bd46d48190915500d75a9d8e94 |
completed | March 6, 2026, 5:10 a.m. |
| PDg | Predicate description generation | batch_69ab752034348190a1cc20955ed24f6f |
completed | March 7, 2026, 12:45 a.m. |
Created at: March 4, 2026, 7:30 p.m.