Triple
T6874078
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Macron family |
E158627
|
entity |
| Predicate | hasNotableProfessionInFamily |
P35389
|
FINISHED |
| Object | politician |
—
|
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: politician | Statement: [Macron family, hasNotableProfessionInFamily, politician]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableProfessionInFamily Context triple: [Macron family, hasNotableProfessionInFamily, politician]
-
A.
hasNotableProfessionField
chosen
Indicates that an entity’s notable profession or occupation belongs to a particular professional field or domain.
-
B.
hasNotableProfessionDistributionIn
Indicates that the distribution or prevalence of notable professions associated with an entity is observed or characterized within a specified context, such as a location or group.
-
C.
hasFamilyRole
Indicates that one entity holds a specific familial role or position in relation to another entity.
-
D.
hasFamilyBackgroundIn
Indicates that an entity comes from, or is associated with, a particular familial or ancestral background.
-
E.
hasChildInSameProfession
Indicates that an individual has at least one child whose profession is the same as their own.
- 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_69c68832af1481908ce356e133ebaebe |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d8c8d3888190b1c1f74aa66d6071 |
completed | March 27, 2026, 7:21 p.m. |
| PD | Predicate disambiguation | batch_69c6d7b363dc8190a7225b540ab2bc40 |
completed | March 27, 2026, 7:17 p.m. |
Created at: March 27, 2026, 2:22 p.m.