Triple
T38006414
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1953 Nanga Parbat expedition |
E948245
|
entity |
| Predicate | hasLanguageOfTeam |
P119688
|
FINISHED |
| Object | German |
—
|
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: German | Statement: [1953 Nanga Parbat expedition, hasLanguageOfTeam, German]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLanguageOfTeam Context triple: [1953 Nanga Parbat expedition, hasLanguageOfTeam, German]
-
A.
languageOfTeamCountry
Indicates that a particular language is the official or primary language used in the country to which a given team belongs.
-
B.
languageOfTeamEnvironment
chosen
Indicates the primary language used for communication and collaboration within a team’s working environment.
-
C.
hasMemberLanguage
Indicates that one entity is a language that is a constituent or member of a larger language group, family, or collection represented by the other entity.
-
D.
hasLanguageOfSide
Indicates that an entity uses or is associated with a particular language on a specific side or aspect (e.g., one side of a bilingual object or interface).
-
E.
hasLanguageGroup
Indicates that an entity belongs to, is associated with, or is categorized under a particular language group.
- 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_69f76efb4b10819092c8c2ba28ac06a8 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a0008225cc081909ff1fd0639859dc4 |
completed | May 10, 2026, 4:22 a.m. |
| PD | Predicate disambiguation | batch_6a0007bac5d8819098aff8031d4abe5d |
completed | May 10, 2026, 4:21 a.m. |
Created at: May 3, 2026, 4:20 p.m.