Triple
T27642172
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nemesis (comic series) |
E696615
|
entity |
| Predicate | pageCountPerIssue |
P162789
|
FINISHED |
| Object | approximately 32 pages |
—
|
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: approximately 32 pages | Statement: [Nemesis (comic series), pageCountPerIssue, approximately 32 pages]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pageCountPerIssue Context triple: [Nemesis (comic series), pageCountPerIssue, approximately 32 pages]
-
A.
mainSeriesIssueCount
Indicates the total number of issues contained in the primary or main series associated with an entity.
-
B.
magazineIssueNumber
Indicates the specific issue number assigned to a magazine within its publication sequence.
-
C.
pageCountFirstEdition
Indicates the number of pages contained in the first edition of an item.
-
D.
journalIssue
Indicates that one entity is a specific issue (numbered or dated installment) of a particular journal or periodical.
-
E.
pageCountUSHardcover
Indicates the number of pages in the U.S. hardcover edition of a work.
- 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_69ef5909f3848190805f35b76833e722 |
completed | April 27, 2026, 12:39 p.m. |
| NER | Named-entity recognition | batch_69f63191966081909f935a352758aaf1 |
completed | May 2, 2026, 5:17 p.m. |
| PD | Predicate disambiguation | batch_69f62c1921008190a62675a31f66a875 |
completed | May 2, 2026, 4:53 p.m. |
| PDg | Predicate description generation | batch_69f62d14c24c81909e86678c1b5fd429 |
completed | May 2, 2026, 4:57 p.m. |
Created at: April 27, 2026, 2:27 p.m.