Triple
T12300762
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lënapei Lënu |
E293213
|
entity |
| Predicate | semanticScope |
P61710
|
FINISHED |
| Object | people and language together |
—
|
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: people and language together | Statement: [Lënapei Lënu, semanticScope, people and language together]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: semanticScope Context triple: [Lënapei Lënu, semanticScope, people and language together]
-
A.
scopeOfReference
chosen
Indicates the range or domain of things, concepts, or entities to which a reference, statement, or expression applies.
-
B.
encodingScope
Indicates the range or extent of content or information that is covered, represented, or captured by a particular encoding.
-
C.
semanticRootMeaning
Indicates the fundamental or core meaning that underlies a word, phrase, or expression in a semantic structure.
-
D.
linguisticScope
Indicates the range or domain within language (such as a phrase, clause, or discourse segment) over which a particular linguistic element, feature, or operation has effect.
-
E.
semanticType
Indicates that something belongs to or is categorized under a particular semantic class or type based on its meaning.
- 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_69d6ab6a2b50819082f6aedd32ed608a |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f621570819091ee1db2609233ea |
completed | April 10, 2026, 6:20 p.m. |
| PD | Predicate disambiguation | batch_69d93ec02c008190a56aae60a3d9eff6 |
completed | April 10, 2026, 6:17 p.m. |
Created at: April 8, 2026, 9:53 p.m.