Triple
T17742310
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zhujiang |
E442890
|
entity |
| Predicate | rankByDischargeInChina |
P20912
|
FINISHED |
| Object | among the largest |
—
|
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: among the largest | Statement: [Zhujiang, rankByDischargeInChina, among the largest]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankByDischargeInChina Context triple: [Zhujiang, rankByDischargeInChina, among the largest]
-
A.
rankingByLengthInChina
Indicates that entities are ordered or evaluated based on their length within the context of China.
-
B.
dischargeRanking
chosen
Indicates the relative level or position of an entity in an ordered list based on the amount or rate of discharge (such as emissions, effluents, or released substances).
-
C.
rankInChineseAdministrativeHierarchy
Indicates the relative level or position an administrative unit holds within the formal hierarchy of Chinese government administration.
-
D.
dischargeRank
Indicates the rank or status an individual held at the time they were discharged from a role, service, or organization.
-
E.
rankInChinaByArea
Indicates the position of an entity in an ordered list of entities in China when sorted by their area size.
- 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_69d8b9ed3a2081909b2ec0d4dd2f4c37 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e47ace58988190b927ca29af7a8b77 |
completed | April 19, 2026, 6:48 a.m. |
| PD | Predicate disambiguation | batch_69e3cde815e08190881972e2d80d151e |
completed | April 18, 2026, 6:31 p.m. |
Created at: April 10, 2026, 10:09 a.m.