Triple
T7351927
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Al Mahmud |
E169521
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Kaler Kalosh
Kaler Kalosh is a renowned poetry collection by Bangladeshi poet Al Mahmud, celebrated for its powerful exploration of Bengali identity, history, and social reality.
|
E659097
|
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: Kaler Kalosh | Statement: [Al Mahmud, notableWork, Kaler Kalosh]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kaler Kalosh Context triple: [Al Mahmud, notableWork, Kaler Kalosh]
-
A.
David Kahler
David Kahler is an American architect best known for his significant contributions to the design and expansion of the Milwaukee Art Museum.
-
B.
Aidan Keller
Aidan Keller is the young, psychically sensitive boy central to the plot of the horror film "The Ring."
-
C.
Sage Kotsenburg
Sage Kotsenburg is an American snowboarder best known for winning the first-ever Olympic gold medal in men's slopestyle at the 2014 Winter Olympics in Sochi.
-
D.
Jordan Kerner
Jordan Kerner is an American film and television producer known for projects such as "Less Than Zero" and the live-action "The Smurfs" films.
-
E.
Josh Kramon
Josh Kramon is a television and film composer best known for scoring the cult mystery series "Veronica Mars."
- 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: Kaler Kalosh Triple: [Al Mahmud, notableWork, Kaler Kalosh]
Generated description
Kaler Kalosh is a renowned poetry collection by Bangladeshi poet Al Mahmud, celebrated for its powerful exploration of Bengali identity, history, and social reality.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kaler Kalosh Target entity description: Kaler Kalosh is a renowned poetry collection by Bangladeshi poet Al Mahmud, celebrated for its powerful exploration of Bengali identity, history, and social reality.
-
A.
David Kahler
David Kahler is an American architect best known for his significant contributions to the design and expansion of the Milwaukee Art Museum.
-
B.
Aidan Keller
Aidan Keller is the young, psychically sensitive boy central to the plot of the horror film "The Ring."
-
C.
Sage Kotsenburg
Sage Kotsenburg is an American snowboarder best known for winning the first-ever Olympic gold medal in men's slopestyle at the 2014 Winter Olympics in Sochi.
-
D.
Jordan Kerner
Jordan Kerner is an American film and television producer known for projects such as "Less Than Zero" and the live-action "The Smurfs" films.
-
E.
Josh Kramon
Josh Kramon is a television and film composer best known for scoring the cult mystery series "Veronica Mars."
- 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_69c68a5878888190968ce4d04db8d69f |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f10b4adc81909a5a0eacaf2b1887 |
completed | March 27, 2026, 9:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7fa99a9148190b67e49c8c8674042 |
completed | March 28, 2026, 3:58 p.m. |
| NEDg | Description generation | batch_69c7fbbff6f081909b694dea7e572ec2 |
completed | March 28, 2026, 4:03 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c7fc2f66248190bac3fa24d530b938 |
completed | March 28, 2026, 4:05 p.m. |
Created at: March 27, 2026, 3:05 p.m.