Triple
T19913674
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Have You Ever Really Loved a Woman? |
E478609
|
entity |
| Predicate | hasBilingualElements |
P82135
|
FINISHED |
| Object | English-Spanish |
—
|
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-Spanish | Statement: [Have You Ever Really Loved a Woman?, hasBilingualElements, English-Spanish]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBilingualElements Context triple: [Have You Ever Really Loved a Woman?, hasBilingualElements, English-Spanish]
-
A.
isBilingual
Indicates that an entity is able to communicate fluently in two distinct languages.
-
B.
isBilingualRegion
Indicates that a region officially uses two languages or has two predominant languages in regular use.
-
C.
isMultilingual
Indicates that an entity can understand and/or communicate in multiple languages.
-
D.
bilingualLayout
chosen
Indicates a layout or arrangement that simultaneously presents content in two different languages.
-
E.
usesBilingualInstruction
Indicates that an entity employs two languages as the medium of instruction within an educational or communicative context.
- 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_69d8e520682081909892916424699bd5 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e659928030819085a4aafc6a0ef5c8 |
completed | April 20, 2026, 4:51 p.m. |
| PD | Predicate disambiguation | batch_69e537ecda248190895c96afb6243823 |
completed | April 19, 2026, 8:15 p.m. |
Created at: April 10, 2026, 1:53 p.m.