Triple
T35725793
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 延河 |
E1032607
|
entity |
| Predicate | 相关历史事件 |
P2107
|
FINISHED |
| Object | 中国共产党在延安时期的革命活动 |
—
|
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: 中国共产党在延安时期的革命活动 | Statement: [延河, 相关历史事件, 中国共产党在延安时期的革命活动]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 相关历史事件 Context triple: [延河, 相关历史事件, 中国共产党在延安时期的革命活动]
-
A.
hasHistoricalEvent
chosen
Indicates that a historical event occurred in, is associated with, or is relevant to a particular entity.
-
B.
associatedEpicEvent
Indicates that one entity is linked or connected to a particular epic event in a relevant or meaningful way.
-
C.
linkedToHistoricalConflict
Indicates that one entity has a documented association or connection with a past historical conflict involving another entity.
-
D.
historicalEventsDescribed
Indicates that one entity (such as a document, text, or account) describes or recounts the historical events associated with another entity.
-
E.
relatedMilitaryEvent
Indicates that two or more entities are connected through the same military event, operation, or conflict, such that one is contextually or causally related to that military occurrence involving the other.
- 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_69f76e102b5881909e5d63a30a5cecbe |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7a34f8ee08190a040304635539a8f |
completed | May 3, 2026, 7:34 p.m. |
| PD | Predicate disambiguation | batch_69f7a06f125c8190843af194f042a465 |
completed | May 3, 2026, 7:22 p.m. |
Created at: May 3, 2026, 4:05 p.m.