Triple
T27756652
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sukuma–Nyamwezi cultural complex |
E701349
|
entity |
| Predicate | sharesLinguisticFeaturesBetween |
P114100
|
FINISHED |
| Object | Sukuma language |
—
|
NE NERFINISHED |
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: Sukuma language | Statement: [Sukuma–Nyamwezi cultural complex, sharesLinguisticFeaturesBetween, Sukuma language]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sharesLinguisticFeaturesBetween Context triple: [Sukuma–Nyamwezi cultural complex, sharesLinguisticFeaturesBetween, Sukuma language]
-
A.
hasDialectalFeaturesSharedWith
chosen
Indicates that two language varieties share specific dialectal features or characteristics in common.
-
B.
sharesLinguisticFamilyWith
Indicates that two languages belong to the same linguistic family or branch within a language family.
-
C.
hasCommonLoanwordsFrom
Indicates that two languages share loanwords that originate from the same source language.
-
D.
shareLanguageInfluence
Indicates that two entities affect or shape each other’s language use, development, or characteristics through mutual or shared influence.
-
E.
linguisticFeature
Indicates a relationship where a linguistic property, pattern, or characteristic is attributed to or associated with a language-related entity (such as a word, phrase, or text).
- 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_69ef6a5193808190816eb7d0020b2d87 |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69fd2a215d6c8190a1a428ccaee603f1 |
completed | May 8, 2026, 12:11 a.m. |
| PD | Predicate disambiguation | batch_69fd28ef19688190bb8370f2812a43e7 |
completed | May 8, 2026, 12:06 a.m. |
Created at: April 27, 2026, 4:23 p.m.