Triple
T31992183
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 鄢 |
E816900
|
entity |
| Predicate | hasVariantPronunciation |
P35086
|
FINISHED |
| Object | no common standard variants in Mandarin |
—
|
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: no common standard variants in Mandarin | Statement: [鄢, hasVariantPronunciation, no common standard variants in Mandarin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVariantPronunciation Context triple: [鄢, hasVariantPronunciation, no common standard variants in Mandarin]
-
A.
hasPronunciationDifferenceFrom
Indicates that two linguistic items differ in how they are pronounced.
-
B.
hasVariantSpelling
Indicates that one term is an alternative spelling form of another term.
-
C.
hasPronunciationInformation
Indicates that there is available information describing how something is pronounced.
-
D.
hasVariantReadingsWith
Indicates a relationship where two textual items are linked because they exhibit differing or alternative readings of (typically) the same underlying content.
-
E.
hasAlternativeVocalization
chosen
Indicates that an entity has another valid way it can be vocalized or pronounced, distinct from its primary or standard vocalization.
- 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_69f348f8002081909a3588758ba94afb |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6bbbef7a88190b0affdec1d41c1e0 |
completed | May 3, 2026, 3:06 a.m. |
| PD | Predicate disambiguation | batch_69f6ba6cef208190bc5cd43d96127004 |
completed | May 3, 2026, 3:01 a.m. |
Created at: May 1, 2026, 12:13 a.m.