Triple
T13513267
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 中国人民解放军建军节 |
E322691
|
entity |
| Predicate | 首次设立地点 |
P8094
|
FINISHED |
| Object | 中华苏维埃共和国临时中央政府 |
—
|
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: 中华苏维埃共和国临时中央政府 | Statement: [中国人民解放军建军节, 首次设立地点, 中华苏维埃共和国临时中央政府]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 首次设立地点 Context triple: [中国人民解放军建军节, 首次设立地点, 中华苏维埃共和国临时中央政府]
-
A.
firstSeriesLocation
Indicates the location where a series first took place, was set, or was initially released.
-
B.
firstLocation
chosen
Indicates the initial or primary place where an entity is situated, originates, or where an event or relationship begins.
-
C.
firstAppearsAtLocation
Indicates the initial location where an entity is first observed, introduced, or comes into existence within a given context or sequence.
-
D.
firstInstalledInCity
Indicates that an entity was initially installed or put into operation in a particular city before any other location.
-
E.
openedFirstBoutiqueIn
Indicates that an entity established its first boutique or retail store in a specified location.
- 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_69d80766a21881909f21a1b7421d3b8a |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaf87ca288190a147fbdb2f90985f |
completed | April 12, 2026, 2:43 p.m. |
| PD | Predicate disambiguation | batch_69dbae0b63748190b5e207f84b2532ea |
completed | April 12, 2026, 2:36 p.m. |
Created at: April 9, 2026, 9:44 p.m.