Triple
T29800089
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lucia di Lammermoor |
E756666
|
entity |
| Predicate | FrenchVersionTitle |
P7000
|
FINISHED |
| Object | Lucie de Lammermoor |
—
|
NE NERFINISHED |
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: Lucie de Lammermoor | Statement: [Lucia di Lammermoor, FrenchVersionTitle, Lucie de Lammermoor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: FrenchVersionTitle Context triple: [Lucia di Lammermoor, FrenchVersionTitle, Lucie de Lammermoor]
-
A.
equivalentTitleInFrench
chosen
Indicates that one entity’s title is the equivalent or corresponding title of another entity, specifically expressed in French.
-
B.
nameInFrench
Indicates that an entity is known or referred to by a specific name expressed in the French language.
-
C.
FrenchForm
Indicates that one entity is a form, version, or expression of another specifically in the French language.
-
D.
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).
-
E.
titleInLanguage
Indicates that a specific title or name is expressed in a particular language.
- 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_69f22454583081908927516cb9938d1d |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f67525c9a0819084e47299fa5dabfe |
completed | May 2, 2026, 10:05 p.m. |
| PD | Predicate disambiguation | batch_69f66ac1a4fc81909740d2e52fbe6970 |
completed | May 2, 2026, 9:21 p.m. |
Created at: April 29, 2026, 5:17 p.m.