Triple
T22924843
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Liguo |
E569273
|
entity |
| Predicate | mayHaveMultipleCharacterForms |
P59069
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Liguo, mayHaveMultipleCharacterForms, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mayHaveMultipleCharacterForms Context triple: [Liguo, mayHaveMultipleCharacterForms, true]
-
A.
hasDistinctLetterForms
Indicates that the related writing system or symbol set uses different visual shapes or styles for the same letter in different contexts (such as position, case, or usage).
-
B.
hasLetterforms
Indicates a relationship where one entity possesses or includes specific letterforms as part of its written or typographic representation.
-
C.
canBeWrittenWithMultipleKanji
chosen
Indicates that the same word or expression can be represented using more than one distinct kanji spelling.
-
D.
canRepresentMultipleChineseCharacters
Indicates that a given form (such as a sound, syllable, or written unit) is capable of corresponding to more than one distinct Chinese character.
-
E.
hasMultipleChineseCharacters
Indicates that the referenced item consists of more than one Chinese character.
- 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_69e2458f7d008190901dccbaebeaba24 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f180d84fc08190b747c3f9052c657a |
completed | April 29, 2026, 3:54 a.m. |
| PD | Predicate disambiguation | batch_69ef3b7c5fc081909ac50c5c8569cc19 |
completed | April 27, 2026, 10:33 a.m. |
Created at: April 17, 2026, 3:43 p.m.