Triple
T10613349
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kara |
E276058
|
entity |
| Predicate | hasNoBeginningOrEnd |
P94967
|
FINISHED |
| Object | symbolic yes |
—
|
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: symbolic yes | Statement: [Kara, hasNoBeginningOrEnd, symbolic yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNoBeginningOrEnd Context triple: [Kara, hasNoBeginningOrEnd, symbolic yes]
-
A.
hasEnd
Indicates that one entity serves as the terminal point, boundary, or conclusion of another entity or process.
-
B.
hasEnding
Indicates that one entity concludes with, or terminates in, another entity (such as a specific substring, segment, or final component).
-
C.
hasNoText
Indicates that the referenced entity or element contains no textual content.
-
D.
hasApproximateEnd
Indicates that an entity’s end point, time, or boundary is known only approximately rather than precisely.
-
E.
hasAmbiguousEnding
Indicates that the event, story, or situation concludes in a way that is open to multiple interpretations or lacks a clear, definitive resolution.
- 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_69d6aaf948d88190806cc3a8c47a3fb2 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d6df5ca94c8190aa3771925913defc |
completed | April 8, 2026, 11:06 p.m. |
| PD | Predicate disambiguation | batch_69d6dd7a223c8190854409d76368f3e8 |
completed | April 8, 2026, 10:58 p.m. |
| PDg | Predicate description generation | batch_69d6df463ea8819091d6683e476b4f21 |
completed | April 8, 2026, 11:05 p.m. |
Created at: April 8, 2026, 7:33 p.m.