Triple
T8674868
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hawraman region |
E205888
|
entity |
| Predicate | hasAlternativeName |
P39
|
FINISHED |
| Object |
Avroman
Avroman is a mountainous cultural and historical region in western Iran and northeastern Iraq, known for its distinct Hawrami (Avromani) Kurdish dialect and rich traditional heritage.
|
E749153
|
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: Avroman | Statement: [Hawraman region, hasAlternativeName, Avroman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Avroman Context triple: [Hawraman region, hasAlternativeName, Avroman]
-
A.
Romão
Romão is a Portuguese given name and surname derived from the Latin name Romanus, commonly associated with Roman heritage.
-
B.
Romana
Romana is a highly intelligent and compassionate Time Lady from the Doctor Who universe who serves as one of the Doctor’s most capable and scholarly companions.
-
C.
Romana
Romana is a small municipality in the Logudoro region of Sardinia, Italy, known for its rural landscape and traditional Sardinian character.
-
D.
Rumuola
Rumuola is a prominent urban neighborhood and transport hub in Port Harcourt, Rivers State, Nigeria.
-
E.
Román
Román is a given name and surname of Latin origin, commonly used in Spanish-speaking 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: Avroman Triple: [Hawraman region, hasAlternativeName, Avroman]
Generated description
Avroman is a mountainous cultural and historical region in western Iran and northeastern Iraq, known for its distinct Hawrami (Avromani) Kurdish dialect and rich traditional heritage.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Avroman Target entity description: Avroman is a mountainous cultural and historical region in western Iran and northeastern Iraq, known for its distinct Hawrami (Avromani) Kurdish dialect and rich traditional heritage.
-
A.
Romão
Romão is a Portuguese given name and surname derived from the Latin name Romanus, commonly associated with Roman heritage.
-
B.
Romana
Romana is a small municipality in the Logudoro region of Sardinia, Italy, known for its rural landscape and traditional Sardinian character.
-
C.
Romana
Romana is a highly intelligent and compassionate Time Lady from the Doctor Who universe who serves as one of the Doctor’s most capable and scholarly companions.
-
D.
Rumuola
Rumuola is a prominent urban neighborhood and transport hub in Port Harcourt, Rivers State, Nigeria.
-
E.
Román
Román is a given name and surname of Latin origin, commonly used in Spanish-speaking 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_69ca83529a9c8190b5c075b4f14636ed |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc49f54dfc8190b7a61e7ed1cfcbeb |
completed | March 31, 2026, 10:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cecd4719a881908ee6bd8514ab9f81 |
completed | April 2, 2026, 8:10 p.m. |
| NEDg | Description generation | batch_69ceceaab52c819082ecea1bcc38def8 |
completed | April 2, 2026, 8:16 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cecf962f28819090fb93b6a7a2784b |
completed | April 2, 2026, 8:20 p.m. |
Created at: March 30, 2026, 6:31 p.m.