Triple
T29256316
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 邓艾 |
E741713
|
entity |
| Predicate | servedInState |
P83665
|
FINISHED |
| Object | 曹魏 |
—
|
NE NERFINISHED |
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: 曹魏 | Statement: [邓艾, servedInState, 曹魏]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servedInState Context triple: [邓艾, servedInState, 曹魏]
-
A.
servedInRole
Indicates that one entity performed duties or held a position within a specified role or office in relation to another entity.
-
B.
servedInCampaign
Indicates that an individual participated in and rendered service during a specific military or organizational campaign.
-
C.
servedInCongress
Indicates that an individual held an official legislative position as a member of a national congress during some period of time.
-
D.
servedInGovernmentWith
Indicates that two individuals held positions in the same government or administration during an overlapping period of time.
-
E.
hasGovernedState
chosen
Indicates that a person or governing body has exercised official governing authority over a particular state or political territory.
- 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_69f0912065c08190bddd23e20e8ef18e |
completed | April 28, 2026, 10:51 a.m. |
| NER | Named-entity recognition | batch_69fdbc5ef46c8190bbcfb9798f4615b7 |
completed | May 8, 2026, 10:35 a.m. |
| PD | Predicate disambiguation | batch_69fdbb270338819082ce3f73903e884f |
completed | May 8, 2026, 10:29 a.m. |
Created at: April 28, 2026, 12:38 p.m.