Triple
T21517307
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kléber (Paris Métro) |
E530877
|
entity |
| Predicate | hasNativeNameLanguageCode |
P13919
|
FINISHED |
| Object | fr |
—
|
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: fr | Statement: [Kléber (Paris Métro), hasNativeNameLanguageCode, fr]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNativeNameLanguageCode Context triple: [Kléber (Paris Métro), hasNativeNameLanguageCode, fr]
-
A.
hasLocaleName
Indicates that an entity has a specific name or label used in a particular language or regional locale.
-
B.
hasNameInLocalLanguage
Indicates that an entity is associated with a name expressed in the local or native language of a given context or region.
-
C.
hasLinguisticCode
chosen
Indicates that an entity is associated with a specific linguistic identifier or code (such as a language or script code) that characterizes its linguistic properties.
-
D.
hasEndonymLanguage
Indicates that the language specified is the one in which a name or term is expressed in its own native or local form.
-
E.
hasCodenameLanguage
Indicates that a codename is expressed or defined in a particular 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_69e0c45d95a081908e7962ad215da746 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ee814278e08190a66d516bed0726b5 |
completed | April 26, 2026, 9:18 p.m. |
| PD | Predicate disambiguation | batch_69e6320043bc81909417c41a718652ba |
completed | April 20, 2026, 2:02 p.m. |
Created at: April 16, 2026, 6:25 p.m.