Triple
T3719341
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Li |
E81602
|
entity |
| Predicate | frequencyRankInChina |
P45181
|
FINISHED |
| Object | one of the most common surnames in China |
—
|
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: one of the most common surnames in China | Statement: [Li, frequencyRankInChina, one of the most common surnames in China]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: frequencyRankInChina Context triple: [Li, frequencyRankInChina, one of the most common surnames in China]
-
A.
rankingByLengthInChina
Indicates that entities are ordered or evaluated based on their length within the context of China.
-
B.
rankingInCountry
chosen
Indicates the position or level an entity holds within an ordered list specific to a particular country.
-
C.
frequencyRankInUnitedStates
Indicates the relative position of something in an ordered list based on how frequently it occurs within the United States.
-
D.
countryRanking
Indicates the relative position or rank assigned to a country within a specific ordered list or comparative evaluation.
-
E.
populationRank
Indicates the relative position of an entity in an ordered list based on the size of its population.
- 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_69ad8b1b7ef081908d2d381bbf54985a |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adca99e4248190a6a91808010b8b05 |
completed | March 8, 2026, 7:14 p.m. |
| PD | Predicate disambiguation | batch_69adc0436e508190909ec4a3e8443aef |
completed | March 8, 2026, 6:30 p.m. |
Created at: March 8, 2026, 3:34 p.m.