Triple
T31992080
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 閻 |
E816898
|
entity |
| Predicate | hasOldChineseReconstruction |
P52962
|
FINISHED |
| Object | *[ɢ]ˤem |
—
|
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: *[ɢ]ˤem | Statement: [閻, hasOldChineseReconstruction, *[ɢ]ˤem]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOldChineseReconstruction Context triple: [閻, hasOldChineseReconstruction, *[ɢ]ˤem]
-
A.
oldChineseReconstruction
chosen
Indicates that something represents a reconstructed form or pronunciation in Old Chinese.
-
B.
middleChineseReconstruction
Indicates a relationship where a form represents the linguist’s reconstructed pronunciation of a word or morpheme in Middle Chinese.
-
C.
hasLexicalReconstruction
Indicates that there exists a hypothesized or reconstructed lexical form corresponding to a word or expression, typically inferred for an earlier or unattested stage of a language.
-
D.
hasReconstructedSoundChanges
Indicates that a set of historical phonological changes has been inferred or reconstructed for a language or language stage.
-
E.
hasFormerRomanization
Indicates that an entity was previously written or represented using an earlier or superseded system of Romanized spelling.
- 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_69f6b49436b0819094e21603054d05d4 |
completed | May 3, 2026, 2:36 a.m. |
| PD | Predicate disambiguation | batch_69f6b3a7bdb481908d16a32f49e38c2c |
completed | May 3, 2026, 2:32 a.m. |
Created at: May 1, 2026, 12:13 a.m.