Triple
T10408654
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leona |
E245330
|
entity |
| Predicate | relatedMeaning |
P10718
|
FINISHED |
| Object | lion |
—
|
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: lion | Statement: [Leona, relatedMeaning, lion]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relatedMeaning Context triple: [Leona, relatedMeaning, lion]
-
A.
commonMeaning
Indicates that multiple entities share the same or very similar meaning or semantic interpretation.
-
B.
possibleMeaning
chosen
Indicates that something may plausibly represent, signify, or be interpreted as a particular meaning or sense.
-
C.
sharesMeaningWith
Indicates that two expressions convey the same or very similar meaning, even if they differ in form or wording.
-
D.
moreCloselyRelatedTo
Indicates that one entity has a stronger or closer relationship, connection, or similarity to a second entity than to some other reference entity.
-
E.
literalMeaningApproximation
Indicates that one entity expresses an approximate or rough literal meaning of another entity, rather than an exact or fully precise interpretation.
- 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_69d381be340c8190b05998703d42d224 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e9faa97c819092cadedadabe26bf |
completed | April 7, 2026, 11:26 a.m. |
| PD | Predicate disambiguation | batch_69d4dfb438c481908dff87c47de2f069 |
completed | April 7, 2026, 10:43 a.m. |
Created at: April 6, 2026, 12:09 p.m.