Triple
T18753914
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 林 |
E458596
|
entity |
| Predicate | frequencyInChinese |
P37986
|
FINISHED |
| Object | common |
—
|
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: common | Statement: [林, frequencyInChinese, common]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: frequencyInChinese Context triple: [林, frequencyInChinese, common]
-
A.
frequencyInUS
Indicates how often something occurs, appears, or is used within the United States.
-
B.
frequencyContent
Indicates that one entity specifies or characterizes the rate or frequency with which the content or occurrence of another entity takes place.
-
C.
frequencyCategory
chosen
Indicates how often an action, event, or relationship occurs, typically by assigning it to a qualitative frequency level (e.g., rare, occasional, frequent).
-
D.
frequencyInHistory
Indicates how often a particular event, state, or relationship has occurred over time within a given historical context.
-
E.
frequencyClass
Indicates how often an event, action, or relation occurs, typically by assigning it to a predefined frequency category or class.
- 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_69d8d394dc308190b6725073f5db324c |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e579ef4ee48190a9892ac9787ffe37 |
completed | April 20, 2026, 12:57 a.m. |
| PD | Predicate disambiguation | batch_69e48d0b7b708190877951b6e6cdcbc4 |
completed | April 19, 2026, 8:06 a.m. |
Created at: April 10, 2026, 11:51 a.m.