Triple
T9052027
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Unicode 3.0 |
E216905
|
entity |
| Predicate | addsBlocks |
P62553
|
FINISHED |
| Object | Yi Syllables |
E748954
|
NE 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: Yi Syllables | Statement: [Unicode 3.0, addsBlocks, Yi Syllables]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yi Syllables Context triple: [Unicode 3.0, addsBlocks, Yi Syllables]
-
A.
Hangul Syllables
Hangul Syllables is the Unicode block that encodes the precomposed modern Korean syllabic characters used for writing Hangul.
-
B.
Yi script
chosen
Yi script is a traditional logographic and syllabic writing system used to represent the Yi languages of southwestern China.
-
C.
Hangul Jamo
Hangul Jamo is a Unicode block that encodes the individual consonant and vowel letters used to write the Korean Hangul script.
-
D.
Hanja
Hanja is the set of traditional Chinese characters historically used to write Korean, especially for proper names, academic terms, and classical texts.
-
E.
Zhuyin
Zhuyin is a phonetic writing system for transcribing the sounds of Mandarin Chinese, primarily used in Taiwan for teaching pronunciation and literacy.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ca83d362e88190ae44b4e4dc194209 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc7a700de48190aa9f61d850e01cbd |
completed | April 1, 2026, 1:52 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfebd1450c819092dccae2c48b100d |
completed | April 3, 2026, 4:33 p.m. |
Created at: March 30, 2026, 7:10 p.m.