Triple
T441049
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | British English |
E10113
|
entity |
| Predicate | hasVocabularyItem |
P7161
|
FINISHED |
| Object | lorry |
—
|
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: lorry | Statement: [British English, hasVocabularyItem, lorry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVocabularyItem Context triple: [British English, hasVocabularyItem, lorry]
-
A.
hasDistinctVocabulary
Indicates that one entity’s vocabulary is different or distinguishable from that of another entity.
-
B.
hasLinguisticElement
chosen
Indicates that one entity includes, is associated with, or is characterized by a particular linguistic component such as a word, phrase, symbol, or other language element.
-
C.
lexicalItem
Indicates that one entity is a word or vocabulary unit associated with, or used to express, another entity (such as a concept, meaning, or linguistic entry).
-
D.
hasTerm
Indicates that an entity includes, is associated with, or is defined by a specific term or condition.
-
E.
hasConcept
Indicates that an entity includes, embodies, or is associated with a particular concept.
- 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_69a2e8465ef481909655c681b01e2986 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2ef2af84881909635ebbbb3465b1b |
completed | Feb. 28, 2026, 1:35 p.m. |
| PD | Predicate disambiguation | batch_69a2eddcf50c8190bfa0d1f8ee9f604a |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:11 p.m.