Triple
T15839273
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ASCA |
E384059
|
entity |
| Predicate | predecessor |
P97
|
FINISHED |
| Object | Ginga |
E384068
|
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: Ginga | Statement: [ASCA, predecessor, Ginga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ginga Context triple: [ASCA, predecessor, Ginga]
-
A.
Ginga
chosen
Ginga was a Japanese X-ray astronomy satellite that conducted important observations of cosmic X-ray sources in the late 1980s and early 1990s.
-
B.
Gaikan
Gaikan is a major orbital expressway route forming part of Tokyo’s ring road system in Japan.
-
C.
Tsukumi
Tsukumi is a coastal city in Japan known for its cement industry and location along the Bungo Channel in Ōita Prefecture on Kyushu Island.
-
D.
Shizumanu Taiyō
Shizumanu Taiyō is a Japanese drama film, based on Toyoko Yamasaki’s novel, that explores corporate corruption and personal integrity within a national airline.
-
E.
Fubuki
Fubuki was a pioneering Japanese Fubuki-class destroyer of the Imperial Japanese Navy, noted for its advanced design and powerful armament that influenced destroyer construction worldwide.
- 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_69d86da34c888190976e06c4019d415a |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e142e4fa24819086a1a226082ac2d3 |
completed | April 16, 2026, 8:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffa13c931481908ed9fd10fddd867c |
completed | May 9, 2026, 9:03 p.m. |
Created at: April 10, 2026, 4:49 a.m.