Triple
T23140447
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | I Have No Mouth, and I Must Scream |
E577444
|
entity |
| Predicate | numberOfHumanCharacters |
P151064
|
FINISHED |
| Object | 5 |
—
|
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: 5 | Statement: [I Have No Mouth, and I Must Scream, numberOfHumanCharacters, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfHumanCharacters Context triple: [I Have No Mouth, and I Must Scream, numberOfHumanCharacters, 5]
-
A.
numberOfCharacters
Indicates the total count of individual characters present in a given text, string, or entity’s representation.
-
B.
numberOfHumanProtagonists
Indicates the count of human characters that serve as protagonists in a given work or context.
-
C.
hasHumanCharacters
Indicates that the subject includes or features characters that are human beings.
-
D.
includesNonHumanCharacters
Indicates that the subject contains or features characters that are not human, such as animals, aliens, or other non-human entities.
-
E.
numberOfPlayableCharacters
Indicates the total count of distinct characters that can be actively controlled or played by a user in a game or interactive experience.
- 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_69e245f8e6248190ba3d58e068b4dccb |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18ec922b481908084eee6a95aef83 |
completed | April 29, 2026, 4:53 a.m. |
| PD | Predicate disambiguation | batch_69ef89f83b108190aaaa1db6221fc163 |
completed | April 27, 2026, 4:08 p.m. |
| PDg | Predicate description generation | batch_69ef9b7494f4819088ae59ea3d0ae8ab |
completed | April 27, 2026, 5:23 p.m. |
Created at: April 17, 2026, 4 p.m.