Triple
T4190719
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Graf (German-speaking countries) |
E89026
|
entity |
| Predicate | equivalentInSpanish |
P28329
|
FINISHED |
| Object | conde |
—
|
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: conde | Statement: [Graf (German-speaking countries), equivalentInSpanish, conde]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: equivalentInSpanish Context triple: [Graf (German-speaking countries), equivalentInSpanish, conde]
-
A.
equivalentInZapotec
Indicates that two linguistic elements are equivalent in meaning or function within the Zapotec language.
-
B.
languageEquivalent
chosen
Indicates that two linguistic expressions convey the same meaning or function across different languages or language varieties.
-
C.
hasNameInSpanish
Indicates that an entity is associated with a specific name expressed in the Spanish language.
-
D.
translationApproximate
Indicates that one entity is an inexact or approximate translation of another, preserving general meaning but not precise wording or full detail.
-
E.
hasSpanishLanguageVersion
Indicates that an entity has a corresponding version or representation available in the Spanish 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_69aed9569a4481908b6c1fcec2a11e21 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af04b009dc8190abda3f149a5b16fa |
completed | March 9, 2026, 5:34 p.m. |
| PD | Predicate disambiguation | batch_69af01935064819096b7619f42e164dd |
completed | March 9, 2026, 5:21 p.m. |
Created at: March 9, 2026, 3:46 p.m.