Triple
T29121030
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Black God’s Kiss |
E737181
|
entity |
| Predicate | wordCountCategory |
P166350
|
FINISHED |
| Object | novelette-length |
—
|
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: novelette-length | Statement: [Black God’s Kiss, wordCountCategory, novelette-length]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wordCountCategory Context triple: [Black God’s Kiss, wordCountCategory, novelette-length]
-
A.
wordCount
Indicates the total number of words contained in a given text or linguistic unit.
-
B.
wordLengthCategory
Indicates the categorical classification of a word based on its length (e.g., short, medium, long).
-
C.
wordCountExcludes
Indicates that certain specified elements are not included when calculating the total number of words.
-
D.
hasCategoryCount
Indicates the number of distinct categories associated with a given entity.
-
E.
wordLength
Indicates that there is a relationship specifying the number of characters (length) in a given word.
- 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_69f077ed54e08190bb02a744e8121a66 |
completed | April 28, 2026, 9:03 a.m. |
| NER | Named-entity recognition | batch_69f661f3e9d48190bea96aeb602ab631 |
completed | May 2, 2026, 8:43 p.m. |
| PD | Predicate disambiguation | batch_69f65c2376a08190be5215171e908e69 |
completed | May 2, 2026, 8:18 p.m. |
| PDg | Predicate description generation | batch_69f65f75ac608190a62cd6afce14f68e |
completed | May 2, 2026, 8:32 p.m. |
Created at: April 28, 2026, 11:25 a.m.