Triple
T28064540
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eugénie de Montijo |
E709207
|
entity |
| Predicate | roleDuringHusbandAbsence |
P73194
|
FINISHED |
| Object | regent of France |
—
|
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: regent of France | Statement: [Eugénie de Montijo, roleDuringHusbandAbsence, regent of France]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleDuringHusbandAbsence Context triple: [Eugénie de Montijo, roleDuringHusbandAbsence, regent of France]
-
A.
roleDuringHusbandBan
Indicates that an entity holds or performs a specific role during the period in which a husband is subject to a ban.
-
B.
roleDuringSpouseTenure
chosen
Indicates that a person held a particular role or position specifically during the period when their spouse was in office or serving in a defined tenure.
-
C.
afterHusbandDeathRole
Indicates the role or status a person assumes after the death of their husband.
-
D.
afterMarriageRole
Indicates the role or status an entity assumes following a marriage event.
-
E.
spouseInWork
Indicates that two entities are spouses within the context of a particular work (such as a book, film, or series), rather than in real life.
- 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_69ef9b6eb6d88190a3fea236eb0f7bed |
completed | April 27, 2026, 5:22 p.m. |
| NER | Named-entity recognition | batch_69f643ed0b7481908cf25f3afec0a61d |
completed | May 2, 2026, 6:35 p.m. |
| PD | Predicate disambiguation | batch_69f641def1e88190a05bf865ced78b23 |
completed | May 2, 2026, 6:26 p.m. |
Created at: April 27, 2026, 8:42 p.m.