Triple
T8407294
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cao Rui |
E198530
|
entity |
| Predicate | eraNameUsed |
P2938
|
FINISHED |
| Object | Taihe |
E701616
|
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: Taihe | Statement: [Cao Rui, eraNameUsed, Taihe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Taihe Context triple: [Cao Rui, eraNameUsed, Taihe]
-
A.
Taihe
chosen
Taihe was a historical Chinese era name used during the Three Kingdoms period under the state of Cao Wei.
-
B.
Izumi
Izumi is a city located in Osaka Prefecture, Japan, known as a residential and commercial hub in the Kansai region.
-
C.
Kagayaki
Kagayaki is the fastest limited-stop train service operating on Japan’s Hokuriku Shinkansen line between Tokyo and the Hokuriku region.
-
D.
Kamogawa
Kamogawa is a coastal city in Chiba Prefecture, Japan, known for its beaches, fishing industry, and the popular Kamogawa Sea World aquarium.
-
E.
Kamogawa
Kamogawa is a prominent river running through Kyoto, Japan, known for its scenic banks, cultural significance, and popular walking paths.
- 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_69ca8310df9c8190b25f16161cca3e41 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cb831409308190981089c303ebaef4 |
completed | March 31, 2026, 8:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce6cdb3b48819091c554539e74812a |
completed | April 2, 2026, 1:19 p.m. |
Created at: March 30, 2026, 6:05 p.m.