Triple
T4389201
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hugging Face Transformers |
E99320
|
entity |
| Predicate | supportsModelType |
P19966
|
FINISHED |
| Object |
Bloom
Bloom is a large open-access multilingual language model developed by the BigScience research workshop for text generation and understanding tasks.
|
E435874
|
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: Bloom | Statement: [Hugging Face Transformers, supportsModelType, Bloom]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bloom Context triple: [Hugging Face Transformers, supportsModelType, Bloom]
-
A.
Bloom
Bloom is a common English and Jewish surname borne by numerous notable figures in literature, academia, and the arts.
-
B.
In Bloom
"In Bloom" is a popular grunge song by Nirvana, known for its heavy guitar riffs and critique of mainstream misinterpretation of the band's music.
-
C.
Bloomy
Bloomy is an informal nickname commonly used to refer to the city of Bloomington, Indiana.
-
D.
The Flower
The Flower is the nickname of Guy Lafleur, the legendary Montreal Canadiens right winger renowned for his speed, scoring prowess, and flowing blond hair.
-
E.
Flourish
Flourish is a positive psychology book by Martin Seligman that outlines his theory of well-being and practical strategies for enhancing happiness and life satisfaction.
- 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: Bloom Triple: [Hugging Face Transformers, supportsModelType, Bloom]
Generated description
Bloom is a large open-access multilingual language model developed by the BigScience research workshop for text generation and understanding tasks.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bloom Target entity description: Bloom is a large open-access multilingual language model developed by the BigScience research workshop for text generation and understanding tasks.
-
A.
Bloom
Bloom is a common English and Jewish surname borne by numerous notable figures in literature, academia, and the arts.
-
B.
In Bloom
"In Bloom" is a popular grunge song by Nirvana, known for its heavy guitar riffs and critique of mainstream misinterpretation of the band's music.
-
C.
Bloomy
Bloomy is an informal nickname commonly used to refer to the city of Bloomington, Indiana.
-
D.
The Flower
The Flower is the nickname of Guy Lafleur, the legendary Montreal Canadiens right winger renowned for his speed, scoring prowess, and flowing blond hair.
-
E.
Flourish
Flourish is a positive psychology book by Martin Seligman that outlines his theory of well-being and practical strategies for enhancing happiness and life satisfaction.
- 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_69b3454f739481909ff6c28331f0c0b9 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35281900c8190882e9ccfa44ab86f |
completed | March 12, 2026, 11:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5e52d63c08190bc98c090cfe0ff1c |
completed | March 14, 2026, 10:46 p.m. |
| NEDg | Description generation | batch_69b5e5b3ba208190b6cb5e40f9e744e8 |
completed | March 14, 2026, 10:48 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b5e62af694819086b3eddb71f591d2 |
completed | March 14, 2026, 10:50 p.m. |
Created at: March 12, 2026, 11:19 p.m.