Triple
T16843246
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Not a Penny More, Not a Penny Less |
E409465
|
entity |
| Predicate | mainCharacterCount |
P29044
|
FINISHED |
| Object | 4 |
—
|
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: 4 | Statement: [Not a Penny More, Not a Penny Less, mainCharacterCount, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainCharacterCount Context triple: [Not a Penny More, Not a Penny Less, mainCharacterCount, 4]
-
A.
numberOfCharacters
Indicates the total count of individual characters present in a given text, string, or entity’s representation.
-
B.
graphicCharactersCount
Indicates the number of printable (non-control) characters present in a given text or string.
-
C.
protagonistCount
chosen
Indicates the number of primary protagonists involved in a given narrative or work.
-
D.
numberOfPlayableCharacters
Indicates the total count of distinct characters that can be actively controlled or played by a user in a game or interactive experience.
-
E.
numberOfHumanProtagonists
Indicates the count of human characters that serve as protagonists in a given work or context.
- 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_69d883952b048190887740a980b712ed |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b35284588190a1d6238438f3e70d |
completed | April 18, 2026, 4:37 p.m. |
| PD | Predicate disambiguation | batch_69e32b87b4248190aaddb05e88452356 |
completed | April 18, 2026, 6:58 a.m. |
Created at: April 10, 2026, 5:24 a.m.