Triple
T14023211
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 空母「赤城」 |
E337386
|
entity |
| Predicate | 識別 |
P9157
|
FINISHED |
| Object | 艦尾に「赤城」の艦名標記 |
—
|
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: 艦尾に「赤城」の艦名標記 | Statement: [空母「赤城」, 識別, 艦尾に「赤城」の艦名標記]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 識別 Context triple: [空母「赤城」, 識別, 艦尾に「赤城」の艦名標記]
-
A.
身分
Indicates a relationship where an entity holds a particular social, legal, or role-based status or identity in relation to a group, institution, or context.
-
B.
helpsIdentify
chosen
Indicates a relationship where one entity serves to distinguish, recognize, or determine the identity or characteristics of another entity.
-
C.
identifierFor
Indicates that one entity serves as a unique identifying label or code for another entity.
-
D.
recognizesIndividual
Indicates that one entity identifies or acknowledges the identity or presence of a specific individual.
-
E.
identifiedUsing
Indicates that one entity was recognized, distinguished, or determined by means of a specified method, tool, or identifier.
- 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_69d81c6543a48190bd5ba93d7419e797 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2f3d87b88190b038d334f4965369 |
completed | April 14, 2026, 12:12 p.m. |
| PD | Predicate disambiguation | batch_69de05a802ac819090604025aae6a4d5 |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 9, 2026, 10:19 p.m.