Triple
T33997069
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | L2 (Barcelona Metro) |
E871699
|
entity |
| Predicate | secondaryLanguageOfName |
P63334
|
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: [L2 (Barcelona Metro), secondaryLanguageOfName, Spanish]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: secondaryLanguageOfName Context triple: [L2 (Barcelona Metro), secondaryLanguageOfName, Spanish]
-
A.
laterSecondaryLanguageOfAdministration
Indicates that one language served as a subsequent or later secondary language used for administrative purposes in relation to another language.
-
B.
alternateLanguageName
chosen
Indicates that an entity has an additional name or label in a different language from its primary or default name.
-
C.
mainLanguageOfAlternativeName
Indicates that a specified language is the primary language in which an alternative name for an entity is expressed.
-
D.
primaryLanguageSide2
Indicates that the second entity in the relationship uses or is associated with the primary language specified.
-
E.
secondaryLanguageRegion
Indicates a region where a language is used in a secondary or supporting role rather than as the primary 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_69f3499f8cbc81908de6ec89fa91ea8f |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69fef3ceef648190b58027c93d757438 |
completed | May 9, 2026, 8:43 a.m. |
| PD | Predicate disambiguation | batch_69fef359da2c819091a034387b08821f |
completed | May 9, 2026, 8:42 a.m. |
Created at: May 1, 2026, 1:50 a.m.