Triple
T19376643
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 赫连 |
E484684
|
entity |
| Predicate | frequencyInModernChina |
P135703
|
FINISHED |
| Object | very low |
—
|
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: very low | Statement: [赫连, frequencyInModernChina, very low]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: frequencyInModernChina Context triple: [赫连, frequencyInModernChina, very low]
-
A.
frequencyInHistory
Indicates how often a particular event, state, or relationship has occurred over time within a given historical context.
-
B.
frequencyInAntiquity
Indicates how often something occurred, appeared, or was used during ancient times.
-
C.
after1949
Indicates that one event, state, or fact occurs or becomes true after the year 1949.
-
D.
numberInModernEra
Indicates that the associated number or count applies specifically to the modern era timeframe, as opposed to historical or earlier periods.
-
E.
ethnicStatusInChina
Indicates the ethnic classification or status that an entity holds within the sociopolitical context of China.
- F. None of above. chosen
Provenance (4 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_69d8e8d460d88190abf0591c5c9d2b0c |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e61a5cfbf48190ac60e3ffa6baa263 |
completed | April 20, 2026, 12:21 p.m. |
| PD | Predicate disambiguation | batch_69e4fd54f8e48190956e73dd8969164a |
completed | April 19, 2026, 4:05 p.m. |
| PDg | Predicate description generation | batch_69e5004c23308190a087b7941a90725f |
completed | April 19, 2026, 4:18 p.m. |
Created at: April 10, 2026, 1:35 p.m.