Triple
T26813695
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Puerto Yankee |
E672064
|
entity |
| Predicate | hasPrimaryLanguageForToponym |
P193771
|
FINISHED |
| Object | 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: Spanish | Statement: [Puerto Yankee, hasPrimaryLanguageForToponym, Spanish]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPrimaryLanguageForToponym Context triple: [Puerto Yankee, hasPrimaryLanguageForToponym, Spanish]
-
A.
hasLanguageOfToponym
Indicates that a place name (toponym) is expressed in or associated with a particular language.
-
B.
hasOfficialLanguageOfToponym
Indicates that a toponym is associated with an official language in which that place name is formally recognized or used.
-
C.
hasPrimaryVernacularLanguageFamily
Indicates that an entity’s main vernacular language belongs to a specified language family.
-
D.
hasPrimaryEthnonym
Indicates that an entity is associated with its main or most commonly used ethnonym (ethnic group name) as its primary designation.
-
E.
hasPrimaryLanguage1
Indicates that an entity’s main or most commonly used language is the specified language.
- F. None of above. chosen
Provenance (4 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_69eeb3225a3c8190aaf6746efeded2f3 |
completed | April 27, 2026, 12:51 a.m. |
| NER | Named-entity recognition | batch_69fd553d7cb881908d243e7a9f30ac85 |
completed | May 8, 2026, 3:15 a.m. |
| PD | Predicate disambiguation | batch_69fd514dcb1c81908333c70d7edd79c9 |
completed | May 8, 2026, 2:58 a.m. |
| PDg | Predicate description generation | batch_69fd553c01488190b9fda48b4a728f04 |
completed | May 8, 2026, 3:15 a.m. |
Created at: April 27, 2026, 4:31 a.m.