Triple
T37610493
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shinji |
E935771
|
entity |
| Predicate | hasVariantSpellings |
P457
|
FINISHED |
| Object | しんじ |
—
|
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: しんじ | Statement: [Shinji, hasVariantSpellings, しんじ]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVariantSpellings Context triple: [Shinji, hasVariantSpellings, しんじ]
-
A.
hasVariantSpelling
chosen
Indicates that one term is an alternative spelling form of another term.
-
B.
hasSpellingVariantFrequency
Indicates a relationship where one spelling variant of a term is associated with how often it occurs relative to other variants.
-
C.
spellingVariantPattern
Indicates a relationship where one form of a word is a systematic spelling variant of another, following a recognizable pattern of orthographic change.
-
D.
hasVariantReadingsWith
Indicates a relationship where two textual items are linked because they exhibit differing or alternative readings of (typically) the same underlying content.
-
E.
hasEthnonymVariant
Indicates that one ethnonym is an alternative or variant form of another ethnonym referring to the same ethnic group.
- 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_69f76ed0a85481909254a8a89090c826 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fbacaf54648190811ea33b34907e8e |
completed | May 6, 2026, 9:03 p.m. |
| PD | Predicate disambiguation | batch_69fba883f770819091059c6f6c6af9f7 |
completed | May 6, 2026, 8:45 p.m. |
Created at: May 3, 2026, 4:18 p.m.