Triple
T12945070
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | final chase of Moby Dick |
E309739
|
entity |
| Predicate | featuresCharacter |
P626
|
FINISHED |
| Object | Flask |
E62710
|
NE 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: Flask | Statement: [final chase of Moby Dick, featuresCharacter, Flask]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Flask Context triple: [final chase of Moby Dick, featuresCharacter, Flask]
-
A.
Flask
Flask is a lightweight, flexible Python micro web framework designed for building web applications and APIs with minimal boilerplate.
-
B.
Flask
chosen
Flask is a minor but tough and pugnacious third mate aboard the whaling ship Pequod in Herman Melville’s novel "Moby-Dick."
-
C.
FLASK
FLASK is a flexible, fine-grained security architecture originally developed for operating systems like SELinux to support configurable mandatory access control policies.
-
D.
Django
Django is a 1966 Italian Spaghetti Western film directed by Sergio Corbucci and starring Franco Nero as a mysterious gunslinger, renowned for its gritty style and influential impact on the genre.
-
E.
Django
Django is a high-level Python web framework that encourages rapid development and clean, pragmatic design for building secure, scalable web applications.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d7bdfb57a88190836b743e2825feca |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97e1b3694819098527dcea3cfed93 |
completed | April 10, 2026, 10:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6c0ec7e8081909fcff6cff11a9337 |
completed | May 3, 2026, 3:28 a.m. |
Created at: April 9, 2026, 5:43 p.m.