Triple
T5092725
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michaël |
E114788
|
entity |
| Predicate | pronouncedSimilarTo |
P8038
|
FINISHED |
| Object | Michael (French pronunciation) |
—
|
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: Michael (French pronunciation) | Statement: [Michaël, pronouncedSimilarTo, Michael (French pronunciation)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pronouncedSimilarTo Context triple: [Michaël, pronouncedSimilarTo, Michael (French pronunciation)]
-
A.
hasPhonologicalSimilarityTo
chosen
Indicates that two linguistic elements share similar sound patterns or phonological features.
-
B.
hasPronunciationDifferenceFrom
Indicates that two linguistic items differ in how they are pronounced.
-
C.
isMorePronouncedIn
Indicates that a particular feature, quality, or effect appears with greater intensity or prominence in one context, entity, or condition than in another.
-
D.
sharesSpellingWith
Indicates that two entities have identical or substantially identical written forms (i.e., they are spelled the same way).
-
E.
hasGrammaticalSimilarityTo
Indicates that two linguistic elements share similar grammatical structure, form, or function.
- 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_69bd443fc49c819089629c00e311310c |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd754369708190bf4e171a904a19e1 |
completed | March 20, 2026, 4:26 p.m. |
| PD | Predicate disambiguation | batch_69bd715c0a448190afc837c6c31dc6ab |
completed | March 20, 2026, 4:10 p.m. |
Created at: March 20, 2026, 1:40 p.m.