Triple
T3360403
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Madame Mère de l’Empereur |
E70706
|
entity |
| Predicate | relatedPersonRole |
P10690
|
FINISHED |
| Object | mother of the reigning emperor |
—
|
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: mother of the reigning emperor | Statement: [Madame Mère de l’Empereur, relatedPersonRole, mother of the reigning emperor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relatedPersonRole Context triple: [Madame Mère de l’Empereur, relatedPersonRole, mother of the reigning emperor]
-
A.
patientRelationship
Indicates that one entity is the patient or recipient of an action, treatment, or service performed by another entity.
-
B.
namedPersonRole
Indicates that a person is identified by name as holding a specific role or position in a given context.
-
C.
termRelationTo
Indicates a general relational association between one term and another, without specifying the exact nature of that relationship.
-
D.
relationshipType
chosen
Indicates the specific kind of relationship that exists between two or more entities.
-
E.
organizationAssociatedWith
Indicates that there is a formal or recognized connection or affiliation between an organization and 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_69ad85a660c48190998489309a3b4869 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb267ec1081909a4e3e227d5bad01 |
completed | March 8, 2026, 5:31 p.m. |
| PD | Predicate disambiguation | batch_69ada4317e288190ab7d0f66e9dba65f |
completed | March 8, 2026, 4:30 p.m. |
Created at: March 8, 2026, 3:13 p.m.