Triple
T4325245
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Armin Ronacher |
E96620
|
entity |
| Predicate | knownFor |
P22
|
FINISHED |
| Object |
Jinja
Jinja is a popular and powerful templating engine for Python, widely used for generating dynamic HTML in web applications and frameworks like Flask.
|
E431935
|
NE FINISHED |
How this triple was built (4 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: [Armin Ronacher, knownFor, Jinja]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jinja Context triple: [Armin Ronacher, knownFor, Jinja]
-
A.
Jinja
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.
-
B.
Jina
Jina is a revered spiritual title in Jainism denoting an enlightened victor who has conquered inner passions and attained omniscience.
-
C.
Jasper
Jasper is a small city in northwestern Alabama known historically for its coal mining and role in the state's industrial development.
-
D.
Jasper
Jasper is a small resort town in Alberta, Canada, serving as a gateway and service hub for visitors to the surrounding Rocky Mountains and Jasper National Park.
-
E.
Jasper
Jasper is one of Cruella de Vil’s bumbling henchmen who helps steal Dalmatian puppies in Disney’s "One Hundred and One Dalmatians."
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Jinja Triple: [Armin Ronacher, knownFor, Jinja]
Generated description
Jinja is a popular and powerful templating engine for Python, widely used for generating dynamic HTML in web applications and frameworks like Flask.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jinja Target entity description: Jinja is a popular and powerful templating engine for Python, widely used for generating dynamic HTML in web applications and frameworks like Flask.
-
A.
Jinja
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.
-
B.
Jina
Jina is a revered spiritual title in Jainism denoting an enlightened victor who has conquered inner passions and attained omniscience.
-
C.
Jasper
Jasper is a small city in northwestern Alabama known historically for its coal mining and role in the state's industrial development.
-
D.
Jasper
Jasper is a small resort town in Alberta, Canada, serving as a gateway and service hub for visitors to the surrounding Rocky Mountains and Jasper National Park.
-
E.
Jasper
Jasper is one of Cruella de Vil’s bumbling henchmen who helps steal Dalmatian puppies in Disney’s "One Hundred and One Dalmatians."
- F. None of above. chosen
Provenance (5 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_69b5d094f5fc819083eddc234f46f6a1 |
completed | March 14, 2026, 9:18 p.m. |
| NEDg | Description generation | batch_69b5d10a20248190b3214509cb637ff4 |
completed | March 14, 2026, 9:20 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b5d4f08ac4819097e71276be11403d |
completed | March 14, 2026, 9:36 p.m. |
Created at: March 12, 2026, 11:13 p.m.