Triple
T12755055
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bujagali Falls |
E304836
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Jinja |
E262127
|
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: Jinja | Statement: [Bujagali Falls, locatedNear, Jinja]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jinja Context triple: [Bujagali Falls, locatedNear, Jinja]
-
A.
Jinja
Jinja is a popular and powerful templating engine for Python, widely used for generating dynamic HTML in web applications and frameworks like Flask.
-
B.
Jinja
chosen
Jinja is a major town in southeastern Uganda, known as a key industrial center and a popular tourist destination near the source of the Nile River.
-
C.
Nunjucks
Nunjucks is a powerful JavaScript templating engine, inspired by Jinja2, commonly used to generate dynamic HTML in web applications and design systems.
-
D.
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.
-
E.
Jínova
Jínova is a municipal district within the municipality of San Juan de la Maguana in the San Juan Province of the Dominican Republic.
- 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_69d7bdf1fcd081909ffb0e0d6fa3a07d |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96d89ea70819098c470344f172167 |
completed | April 10, 2026, 9:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f67c9aa6308190bfcb1511a561c0f9 |
completed | May 2, 2026, 10:37 p.m. |
Created at: April 9, 2026, 5:27 p.m.