Triple
T28226997
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Femme à la Robe Verte |
E711614
|
entity |
| Predicate | hasAlternativeTitleLanguage |
P35269
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [La Femme à la Robe Verte, hasAlternativeTitleLanguage, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAlternativeTitleLanguage Context triple: [La Femme à la Robe Verte, hasAlternativeTitleLanguage, English]
-
A.
haveAlternativeTitle
Indicates that an entity is known by one or more alternative titles or names in addition to its primary title.
-
B.
hasAlternateTitleRegion
Indicates that an entity has an alternate title that is specifically used or valid within a particular geographic region.
-
C.
hasAlternativeTitleCombination
Indicates that an entity is associated with one or more alternative titles considered together as a specific combination or set.
-
D.
languageOfAlternativeTitle
chosen
Indicates the language in which an alternative or variant title of an entity is expressed.
-
E.
hasAlternativeEditionTitle
Indicates that an entity has a different or variant title used in another edition of the same work.
- 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_69efb51dfb048190ada79b745c33b363 |
completed | April 27, 2026, 7:12 p.m. |
| NER | Named-entity recognition | batch_69f6d0d46aec819091edf97324d793ac |
completed | May 3, 2026, 4:36 a.m. |
| PD | Predicate disambiguation | batch_69f6cfe2183481908ae4e85a59c66f69 |
completed | May 3, 2026, 4:32 a.m. |
Created at: April 27, 2026, 10:50 p.m.