Triple
T4325313
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pallets Projects |
E96621
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object | MarkupSafe |
E431942
|
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: MarkupSafe | Statement: [Pallets Projects, hasComponent, MarkupSafe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MarkupSafe Context triple: [Pallets Projects, hasComponent, MarkupSafe]
-
A.
MarkupSafe
chosen
MarkupSafe is a Python library that provides a string type with automatic HTML escaping, commonly used in web templating to prevent injection vulnerabilities.
-
B.
itsdangerous
itsdangerous is a Python library for securely signing and serializing data, commonly used in web applications to protect cookies and other trusted data.
-
C.
Jinja2
Jinja2 is a popular Python templating engine used to generate dynamic HTML and other text-based formats, known for its Django-inspired syntax and integration with web frameworks like Flask.
-
D.
Werkzeug WSGI utility library
Werkzeug WSGI utility library is a comprehensive Python toolkit that provides utilities for building WSGI-compliant web applications and frameworks.
-
E.
Jinja
Jinja is a popular and powerful templating engine for Python, widely used for generating dynamic HTML in web applications and frameworks like Flask.
- 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_69b34542fd908190b11b08faad8decfd |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3512ec18481908a7b5c29b3902b53 |
completed | March 12, 2026, 11:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5e4f309988190abda9b4e6a422260 |
completed | March 14, 2026, 10:45 p.m. |
Created at: March 12, 2026, 11:13 p.m.