Triple
T15777766
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wazwan |
E382532
|
entity |
| Predicate | typicalDish |
P19483
|
FINISHED |
| Object |
Tabak Maaz
Tabak Maaz is a traditional Kashmiri dish of twice-cooked lamb ribs, prized for their rich flavor and crisp texture and commonly served as part of the elaborate Wazwan feast.
|
E1177405
|
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: Tabak Maaz | Statement: [Wazwan, typicalDish, Tabak Maaz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tabak Maaz Context triple: [Wazwan, typicalDish, Tabak Maaz]
-
A.
Mazhar
Mazhar is a masculine given name of Arabic origin commonly used in Turkey and several other Muslim-majority countries.
-
B.
Hanif
Hanif is a town and commune located within Bouira Province in northern Algeria.
-
C.
Akthar
Akthar is an individual or entity known primarily through an association with Rudge, likely within a shared professional, academic, or organizational context.
-
D.
Tayeb
Tayeb is the given name of Tayeb Salih, a renowned Sudanese novelist best known for his work "Season of Migration to the North."
-
E.
Tahsin
Tahsin is a masculine given name of Turkish and Arabic origin, commonly used in Turkey and other Muslim-majority countries.
- 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: Tabak Maaz Triple: [Wazwan, typicalDish, Tabak Maaz]
Generated description
Tabak Maaz is a traditional Kashmiri dish of twice-cooked lamb ribs, prized for their rich flavor and crisp texture and commonly served as part of the elaborate Wazwan feast.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tabak Maaz Target entity description: Tabak Maaz is a traditional Kashmiri dish of twice-cooked lamb ribs, prized for their rich flavor and crisp texture and commonly served as part of the elaborate Wazwan feast.
-
A.
Mazhar
Mazhar is a masculine given name of Arabic origin commonly used in Turkey and several other Muslim-majority countries.
-
B.
Hanif
Hanif is a town and commune located within Bouira Province in northern Algeria.
-
C.
Akthar
Akthar is an individual or entity known primarily through an association with Rudge, likely within a shared professional, academic, or organizational context.
-
D.
Tayeb
Tayeb is the given name of Tayeb Salih, a renowned Sudanese novelist best known for his work "Season of Migration to the North."
-
E.
Tahsin
Tahsin is a masculine given name of Turkish and Arabic origin, commonly used in Turkey and other Muslim-majority countries.
- 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_69d86da09a10819082fe9797b23e4664 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e05199cd8881909462462cec34d35a |
completed | April 16, 2026, 3:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff909d7b7c81908b62faa2ab378fad |
completed | May 9, 2026, 7:53 p.m. |
| NEDg | Description generation | batch_69ff9414408c8190903421d28519d9e6 |
completed | May 9, 2026, 8:07 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff9498642081909fd617a327b4c704 |
completed | May 9, 2026, 8:10 p.m. |
Created at: April 10, 2026, 4:47 a.m.