Triple
T23626012
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clypeosphaeriaceae |
E583466
|
entity |
| Predicate | hasAsci |
P153326
|
FINISHED |
| Object | unitunicate asci |
—
|
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: unitunicate asci | Statement: [Clypeosphaeriaceae, hasAsci, unitunicate asci]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAsci Context triple: [Clypeosphaeriaceae, hasAsci, unitunicate asci]
-
A.
asciType
Indicates that one entity has a specific ASCII character type or classification in relation to another entity.
-
B.
hasAscentCharacter
Indicates that something possesses a particular type or quality of ascent, such as how it rises, climbs, or increases.
-
C.
hasUnicode
Indicates that an entity is associated with, represented by, or encoded using a specific Unicode character or sequence.
-
D.
hasAccent
Indicates that an entity speaks with or possesses a particular accent or distinctive pronunciation style.
-
E.
ASCIICode
Indicates that one entity is the ASCII numerical code corresponding to the character represented by the other entity.
- F. None of above. chosen
Provenance (4 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_69e248fc8d74819091bd5baef2f36f6f |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1b17d4f508190abbb508746bfebb9 |
completed | April 29, 2026, 7:21 a.m. |
| PD | Predicate disambiguation | batch_69f118d0e0588190a86527a7747c5427 |
completed | April 28, 2026, 8:30 p.m. |
| PDg | Predicate description generation | batch_69f138b8c9248190b059bc38a9a50958 |
completed | April 28, 2026, 10:46 p.m. |
Created at: April 17, 2026, 6:46 p.m.