Triple
T27167399
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Viejo Mundo |
E682814
|
entity |
| Predicate | tieneEquivalenteEnInglés |
P28329
|
FINISHED |
| Object | Old World |
—
|
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: Old World | Statement: [Viejo Mundo, tieneEquivalenteEnInglés, Old World]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tieneEquivalenteEnInglés Context triple: [Viejo Mundo, tieneEquivalenteEnInglés, Old World]
-
A.
languageEquivalent
chosen
Indicates that two linguistic expressions convey the same meaning or function across different languages or language varieties.
-
B.
equivalentInZapotec
Indicates that two linguistic elements are equivalent in meaning or function within the Zapotec language.
-
C.
equivalentIn
Indicates that two entities are considered logically or functionally the same in meaning, status, or effect within a given context.
-
D.
hasSpanishMeaning
Indicates that one entity serves as the Spanish-language meaning or translation of another entity.
-
E.
EnglishTranslatorOfWork
Indicates that one entity is the person who translated a specific work into English.
- 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_69eefacf6e788190a75a64399d9e3109 |
completed | April 27, 2026, 5:57 a.m. |
| NER | Named-entity recognition | batch_69f6562fd3488190be1acd8c526a28d2 |
completed | May 2, 2026, 7:53 p.m. |
| PD | Predicate disambiguation | batch_69f651a931748190a637e631a52bbfaa |
completed | May 2, 2026, 7:34 p.m. |
Created at: April 27, 2026, 9:21 a.m.