Triple
T11540069
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 毛泽覃 |
E273649
|
entity |
| Predicate | 相关人物 |
P77482
|
FINISHED |
| Object | 毛泽东 |
—
|
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: 毛泽东 | Statement: [毛泽覃, 相关人物, 毛泽东]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 相关人物 Context triple: [毛泽覃, 相关人物, 毛泽东]
-
A.
peopleAssociatedWith
chosen
Indicates that there exists some form of connection, involvement, or relationship between the referenced people.
-
B.
relatedCharacter
Indicates that one character has a specified relationship or association with another character.
-
C.
involvedActor
Indicates that an entity participates as an actor or participant in the referenced event, activity, or situation.
-
D.
relatedCharacterType
Indicates that one character has a specified type of relationship or role in connection to another character.
-
E.
relatedCharacterContext
Indicates a contextual relationship between characters, such as roles, interactions, or situational connections that link them within a specific narrative or setting.
- 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_69d6aae3fbec8190a14632a5df2538b6 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d886deed5c81908e5c38156064f882 |
completed | April 10, 2026, 5:13 a.m. |
| PD | Predicate disambiguation | batch_69d80879fdb48190be6dacc8aa63c809 |
completed | April 9, 2026, 8:13 p.m. |
Created at: April 8, 2026, 9:37 p.m.