Triple
T111457
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mako Mori |
E2255
|
entity |
| Predicate | backstoryEvent |
P259
|
FINISHED |
| Object | survivor of Kaiju attack on Tokyo |
—
|
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: survivor of Kaiju attack on Tokyo | Statement: [Mako Mori, backstoryEvent, survivor of Kaiju attack on Tokyo]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: backstoryEvent Context triple: [Mako Mori, backstoryEvent, survivor of Kaiju attack on Tokyo]
-
A.
hasHistoricalEvent
Indicates that a historical event occurred in, is associated with, or is relevant to a particular entity.
-
B.
significantEvent
chosen
Indicates that an event involving the entities is of notable importance or impact within a given context.
-
C.
historicEventAlsoKnownAs
Indicates that a historic event is referred to by an alternative name or alias.
-
D.
mythologicalEvent
Indicates an event or occurrence that takes place within mythological narratives or traditions, often involving gods, heroes, or supernatural phenomena.
-
E.
storyBy
Indicates that one entity is the creator or author of the story associated with another entity.
- F. None of above.
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_69a24fcdaeb48190a2d796677e4b3281 |
completed | Feb. 28, 2026, 2:15 a.m. |
| NER | Named-entity recognition | batch_69a258b58efc8190959c86f73d67b744 |
completed | Feb. 28, 2026, 2:53 a.m. |
| PD | Predicate disambiguation | batch_69a25641058c8190b5b64509b35d8176 |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:20 a.m.