Triple
T35647935
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Romulan language |
E1030058
|
entity |
| Predicate | hasCanonicalVocabulary |
P200630
|
FINISHED |
| Object | limited on-screen words and phrases |
—
|
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: limited on-screen words and phrases | Statement: [Romulan language, hasCanonicalVocabulary, limited on-screen words and phrases]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCanonicalVocabulary Context triple: [Romulan language, hasCanonicalVocabulary, limited on-screen words and phrases]
-
A.
hasVocabularyFrom
Indicates that one entity’s vocabulary, terminology, or set of terms is derived from, based on, or taken from another entity.
-
B.
hasCanonicalTerm
Indicates that one term in a set is designated as the standard or authoritative form used to represent a concept or entity.
-
C.
hasStandardVocabularyPolicy
Indicates that an entity enforces or follows a defined, organization-wide policy governing the use of standard vocabulary or terminology.
-
D.
hasDistinctVocabulary
Indicates that one entity’s vocabulary is different or distinguishable from that of another entity.
-
E.
hasKnownVocabulary
Indicates that an entity possesses a defined, identifiable set of terms or words that it can recognize or use.
- F. None of above. chosen
Provenance (4 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_69f76e0938088190a8f199631e97dec3 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69ff9bed58dc8190a204816d4ed6c32c |
completed | May 9, 2026, 8:41 p.m. |
| PD | Predicate disambiguation | batch_69ff9b69653c81908ab0d88055a66a88 |
completed | May 9, 2026, 8:39 p.m. |
| PDg | Predicate description generation | batch_69ff9bec9d748190869caebf6bf0c78f |
completed | May 9, 2026, 8:41 p.m. |
Created at: May 3, 2026, 4:05 p.m.