Triple
T27092347
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Xcalakoop Santa Clara |
E686201
|
entity |
| Predicate | likelyPrimaryLanguage |
P83252
|
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: [Xcalakoop Santa Clara, likelyPrimaryLanguage, Spanish]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: likelyPrimaryLanguage Context triple: [Xcalakoop Santa Clara, likelyPrimaryLanguage, Spanish]
-
A.
primaryLanguageOf
Indicates that a specified language is the main or official language used by a particular entity (such as a person, organization, or region).
-
B.
hasPrimaryLanguage1
chosen
Indicates that an entity’s main or most commonly used language is the specified language.
-
C.
primaryLanguageIn
Indicates that a specified language is the main or official language used within a particular place, organization, or context.
-
D.
primaryLanguageImplied
Indicates that an entity’s primary language is inferred or assumed rather than explicitly stated or directly observed.
-
E.
primaryLanguageType
Indicates the main category or kind of language (such as spoken, written, or signed) that serves as the primary mode of communication in a given context or for a given entity.
- 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_69ef148940ec819097b5c20fbfbf7c81 |
completed | April 27, 2026, 7:47 a.m. |
| NER | Named-entity recognition | batch_69f6383625cc8190aa223d8ef655743c |
completed | May 2, 2026, 5:45 p.m. |
| PD | Predicate disambiguation | batch_69f63709e4848190b5cf322e06b23fb6 |
completed | May 2, 2026, 5:40 p.m. |
Created at: April 27, 2026, 8:41 a.m.