Triple
T7824703
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shu Han |
E181217
|
entity |
| Predicate | alternativeName |
P39
|
FINISHED |
| Object | Shu |
E287878
|
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: Shu | Statement: [Shu Han, alternativeName, Shu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shu Context triple: [Shu Han, alternativeName, Shu]
-
A.
Shu
Shu is an ancient Egyptian god of air and light, often depicted separating the sky goddess Nut from the earth god Geb.
-
B.
Shu
chosen
Shu is a Chinese surname borne by numerous individuals across history and contemporary society.
-
C.
Tsu
Tsu is a coastal city in central Japan that serves as the administrative and economic center of Mie Prefecture.
-
D.
Pei
Pei is the family name of I. M. Pei, the renowned Chinese-American architect known for designing landmarks such as the Louvre Pyramid in Paris.
-
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_69ca8282ccec819083c48efb72d21cf9 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cafa0c1f5c8190b16db20daad159a1 |
completed | March 30, 2026, 10:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb14aefd4881908ffa5825f4ba6eff |
completed | March 31, 2026, 12:26 a.m. |
Created at: March 30, 2026, 4:42 p.m.