Triple
T28434233
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Latin-based Zhuang alphabet |
E715217
|
entity |
| Predicate | marksTone |
P67251
|
FINISHED |
| Object | with tone letters and/or diacritics |
—
|
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: with tone letters and/or diacritics | Statement: [Latin-based Zhuang alphabet, marksTone, with tone letters and/or diacritics]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marksTone Context triple: [Latin-based Zhuang alphabet, marksTone, with tone letters and/or diacritics]
-
A.
marksTones
chosen
Indicates that one entity applies or denotes tonal markings or distinctions on another entity, such as in language or notation.
-
B.
marksDetermine
Indicates that one entity’s marks or scores determine or decisively influence the outcome, status, or classification of another entity.
-
C.
marks
Indicates that one entity makes a visible or symbolic sign on, or designates, another entity for identification, emphasis, or distinction.
-
D.
marksOn
Indicates that one entity bears visible signs, traces, or imprints that have been made or left by another entity.
-
E.
markType
Indicates the specific category or kind of mark associated with or applied to an entity.
- 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_69efd6b253888190b3c7222ed6a403a8 |
completed | April 27, 2026, 9:35 p.m. |
| NER | Named-entity recognition | batch_69f6562fd3488190be1acd8c526a28d2 |
completed | May 2, 2026, 7:53 p.m. |
| PD | Predicate disambiguation | batch_69f651a931748190a637e631a52bbfaa |
completed | May 2, 2026, 7:34 p.m. |
Created at: April 28, 2026, 1:41 a.m.