Triple
T18131068
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Japanese destroyer Hatsushimo |
E434010
|
entity |
| Predicate | roleInOperationTen-Go |
P130569
|
FINISHED |
| Object | escort for battleship Yamato |
—
|
LITERAL 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: escort for battleship Yamato | Statement: [Japanese destroyer Hatsushimo, roleInOperationTen-Go, escort for battleship Yamato]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleInOperationTen-Go Context triple: [Japanese destroyer Hatsushimo, roleInOperationTen-Go, escort for battleship Yamato]
-
A.
roleInDiceGame
Indicates the specific function or position an entity holds within the context of a dice game (e.g., shooter, dealer, scorer).
-
B.
roleInGameplay
Indicates the specific function or responsibility an entity has within the context of gameplay or game mechanics.
-
C.
roleInDomino
Indicates that an entity participates in a domino effect or chain of events by occupying a specific role or position within that causal sequence.
-
D.
roleInGemini10
Indicates that an entity holds a specific role or position in the context of the Gemini 10 mission or project.
-
E.
roleInRumisLife
Indicates the specific function, influence, or position an entity holds in Rumi’s life.
- F. None of above. chosen
Provenance (4 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_69d8b909e8cc81908df4cc2b8ea6d11f |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4ddf1e2508190993f65ca137fdf63 |
completed | April 19, 2026, 1:51 p.m. |
| PD | Predicate disambiguation | batch_69e43317d11c81908d1dc14921566b47 |
completed | April 19, 2026, 1:42 a.m. |
| PDg | Predicate description generation | batch_69e438f5ae2c8190b11dee46534fa5a9 |
completed | April 19, 2026, 2:07 a.m. |
Created at: April 10, 2026, 10:29 a.m.