Triple
T12874060
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Battler |
E307918
|
entity |
| Predicate | hasApproximateWordCountCategory |
P67671
|
FINISHED |
| Object | short story (under 10,000 words) |
—
|
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: short story (under 10,000 words) | Statement: [The Battler, hasApproximateWordCountCategory, short story (under 10,000 words)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateWordCountCategory Context triple: [The Battler, hasApproximateWordCountCategory, short story (under 10,000 words)]
-
A.
hasApproximateNumberOfLetters
Indicates that an entity is associated with a number that roughly, but not exactly, corresponds to the count of letters it contains.
-
B.
hasApproximateNumberOfAttestedWords
chosen
Indicates that an entity is associated with an estimated or approximate count of words that are documented or attested for it.
-
C.
wordLengthCategory
Indicates the categorical classification of a word based on its length (e.g., short, medium, long).
-
D.
wordCount
Indicates the total number of words contained in a given text or linguistic unit.
-
E.
hasPageCountApprox
Indicates that an entity is associated with an approximate or estimated number of pages, rather than an exact page count.
- 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_69d7bdf69bc48190af6c2621f28ca351 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97c7f91d08190aac2f6419d3ba992 |
completed | April 10, 2026, 10:41 p.m. |
| PD | Predicate disambiguation | batch_69d96fa55b888190ab1612e93c41aec4 |
completed | April 10, 2026, 9:46 p.m. |
Created at: April 9, 2026, 5:38 p.m.