Triple
T5778885
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Broomhilda von Shaft |
E127508
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Django |
E331529
|
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: Django | Statement: [Broomhilda von Shaft, spouse, Django]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Django Context triple: [Broomhilda von Shaft, spouse, Django]
-
A.
Django
Django is a high-level Python web framework that encourages rapid development and clean, pragmatic design for building secure, scalable web applications.
-
B.
Django
chosen
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.
-
C.
Django Strikes Again
Django Strikes Again is a 1987 Italian Western film that serves as the official sequel to the classic 1966 Spaghetti Western Django, once again starring Franco Nero in the title role.
-
D.
Flask
Flask is a minor but tough and pugnacious third mate aboard the whaling ship Pequod in Herman Melville’s novel "Moby-Dick."
-
E.
Flask
Flask is a lightweight, flexible Python micro web framework designed for building web applications and APIs with minimal boilerplate.
- 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_69c008361fa88190aefa4dc41b051e7f |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c029e107348190a03086f1cbfae0d3 |
completed | March 22, 2026, 5:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0a17276648190b1fedfcc69d46b59 |
completed | March 23, 2026, 2:12 a.m. |
Created at: March 22, 2026, 3:50 p.m.