Triple
T7119360
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mayor of Tianjin |
E165908
|
entity |
| Predicate | rankInPRCSystem |
P45181
|
FINISHED |
| Object | vice-ministerial level |
—
|
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: vice-ministerial level | Statement: [Mayor of Tianjin, rankInPRCSystem, vice-ministerial level]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankInPRCSystem Context triple: [Mayor of Tianjin, rankInPRCSystem, vice-ministerial level]
-
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.
rankingScope
Indicates the context or domain within which a ranking is defined, interpreted, or applied.
-
D.
rankInChinaByArea
Indicates the position of an entity in an ordered list of entities in China when sorted by their area size.
-
E.
rankedAs
Indicates that one entity is assigned a specific position or level in an ordered ranking relative to others.
- 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_69c6888227bc8190a1394679e3116f90 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e61b8a288190a165ea25adfaaef5 |
completed | March 27, 2026, 8:18 p.m. |
| PD | Predicate disambiguation | batch_69c6e1c4f9788190830288d00cc37026 |
completed | March 27, 2026, 8 p.m. |
Created at: March 27, 2026, 2:43 p.m.