Triple
T9260381
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Surah Fussilat |
E222560
|
entity |
| Predicate | hasOpeningLetters |
P27928
|
FINISHED |
| Object |
Ha Mim
Ha Mim is a pair of Arabic disjointed letters that appear at the beginning of several chapters (surahs) of the Qur’an as part of its mysterious opening letter combinations.
|
E787813
|
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: Ha Mim | Statement: [Surah Fussilat, hasOpeningLetters, Ha Mim]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ha Mim Context triple: [Surah Fussilat, hasOpeningLetters, Ha Mim]
-
A.
Hani
The Hani are an ethnic minority group in China, primarily known for their terraced rice farming, distinctive traditional dress, and rich folk culture in the mountainous regions of Yunnan.
-
B.
Mimi Hii
Mimi Hii is a prominent chemist known for her research in catalysis and sustainable chemistry, holding a prestigious professorship at Imperial College London.
-
C.
Hana
Hana is a small, remote town on the eastern coast of Maui, Hawaii, known for its lush landscapes, waterfalls, and the scenic Road to Hana.
-
D.
Hana
Hana is a compassionate Canadian army nurse in Michael Ondaatje's novel "The English Patient," who cares for a badly burned man in an abandoned Italian villa during World War II.
-
E.
Hana
Hana is a person known primarily as the romantic partner of Kip.
- 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: Ha Mim Triple: [Surah Fussilat, hasOpeningLetters, Ha Mim]
Generated description
Ha Mim is a pair of Arabic disjointed letters that appear at the beginning of several chapters (surahs) of the Qur’an as part of its mysterious opening letter combinations.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ha Mim Target entity description: Ha Mim is a pair of Arabic disjointed letters that appear at the beginning of several chapters (surahs) of the Qur’an as part of its mysterious opening letter combinations.
-
A.
Hani
The Hani are an ethnic minority group in China, primarily known for their terraced rice farming, distinctive traditional dress, and rich folk culture in the mountainous regions of Yunnan.
-
B.
Mimi Hii
Mimi Hii is a prominent chemist known for her research in catalysis and sustainable chemistry, holding a prestigious professorship at Imperial College London.
-
C.
Hana
Hana is a small, remote town on the eastern coast of Maui, Hawaii, known for its lush landscapes, waterfalls, and the scenic Road to Hana.
-
D.
Hana
Hana is a person known primarily as the romantic partner of Kip.
-
E.
Hana
Hana is a Japanese restaurant located within Tokyo Disney Resort’s Disney Ambassador Hotel, offering themed dining to hotel guests and park visitors.
- 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_69ca841e4cd481908e738c74e958eaea |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd07160e408190be4bd7b757260a0e |
completed | April 1, 2026, 11:52 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d09bfb7dfc8190bc337a54083e0dd9 |
completed | April 4, 2026, 5:04 a.m. |
| NEDg | Description generation | batch_69d09cc5cdd481908903ae0e49c1085d |
completed | April 4, 2026, 5:08 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d09d7364b48190ad3dd55711bd2534 |
completed | April 4, 2026, 5:11 a.m. |
Created at: March 30, 2026, 7:32 p.m.