Triple
T5171957
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kumano |
E116703
|
entity |
| Predicate | hasSisterShip |
P3142
|
FINISHED |
| Object | Suzuya |
E93463
|
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: Suzuya | Statement: [Kumano, hasSisterShip, Suzuya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Suzuya Context triple: [Kumano, hasSisterShip, Suzuya]
-
A.
Suzuya
chosen
Suzuya is a Japanese Mogami-class heavy cruiser of the Imperial Japanese Navy that served during World War II.
-
B.
Takatsuki
Takatsuki is a city in northern Osaka Prefecture, Japan, known as a residential and commercial hub between Osaka and Kyoto.
-
C.
Takarano
Takarano is a small settlement on the atoll of Tabiteuea in the island nation of Kiribati, located in the central Pacific Ocean.
-
D.
Marunouchi
Marunouchi is a central Tokyo business district known for its concentration of corporate headquarters, upscale offices, and proximity to Tokyo Station and the Imperial Palace.
-
E.
Sakae
Sakae is a major downtown commercial and entertainment district in Nagoya, Japan, known for its shopping, nightlife, and landmark attractions.
- 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_69bd445ff97c81909a2615cc56235470 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd795252a481908634779f3f656574 |
completed | March 20, 2026, 4:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c5187dfe008190ac60e042527e55b3 |
completed | March 26, 2026, 11:29 a.m. |
Created at: March 20, 2026, 1:45 p.m.