Triple
T10315181
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ereuniidae |
E241996
|
entity |
| Predicate | morphologicalSimilarity |
P87414
|
FINISHED |
| Object | scorpionfishes |
—
|
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: scorpionfishes | Statement: [Ereuniidae, morphologicalSimilarity, scorpionfishes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: morphologicalSimilarity Context triple: [Ereuniidae, morphologicalSimilarity, scorpionfishes]
-
A.
morphologicalComparison
chosen
Indicates a relationship where two or more entities are compared based on differences or similarities in their morphological (form or structural) characteristics.
-
B.
hasLexicalSimilarityWith
Indicates that two linguistic items share a significant degree of similarity in form, structure, or wording.
-
C.
hasPhonologicalSimilarityTo
Indicates that two linguistic elements share similar sound patterns or phonological features.
-
D.
hasLetterSetSimilarity
Indicates that two entities share a similar set of letters, typically based on overlap or resemblance between the characters in their textual representations.
-
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_69d381ac38808190a8ca7457c85b625b |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d7ccb7ec8190a538cf279e48116e |
completed | April 7, 2026, 10:09 a.m. |
| PD | Predicate disambiguation | batch_69d4d1f4f354819080b4ed4bc61bdff6 |
completed | April 7, 2026, 9:44 a.m. |
Created at: April 6, 2026, 11:48 a.m.