Triple
T8148107
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sima Yi |
E190263
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Sima |
E706369
|
NE 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: Sima | Statement: [Sima Yi, familyName, Sima]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sima Context triple: [Sima Yi, familyName, Sima]
-
A.
Sima
chosen
Sima is a Chinese surname historically associated with prominent figures such as the Song dynasty historian and statesman Sima Guang.
-
B.
Sima Samar
Sima Samar is an Afghan physician and human rights advocate renowned for her work promoting women's rights, education, and social justice in Afghanistan.
-
C.
Sicong
Sicong is a given name most notably associated with Ma Sicong, a prominent 20th-century Chinese composer and violinist.
-
D.
Ganlu
Ganlu was a historical Chinese era name used during the Cao Wei state of the Three Kingdoms period.
-
E.
Taishi
Taishi is a town in Osaka Prefecture, Japan, known for its historical sites and traditional rural character.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ca82be7ba8819087de0147e9292c83 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb447e74e081908df774edb2134209 |
completed | March 31, 2026, 3:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ccbee697208190a1d9c98b2a4414bd |
completed | April 1, 2026, 6:44 a.m. |
Created at: March 30, 2026, 5:36 p.m.