Triple
T2788882
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sharm El Sheikh |
E61877
|
entity |
| Predicate | hasDistrict |
P459
|
FINISHED |
| Object |
Hadaba
Hadaba is a coastal district of Sharm El Sheikh in Egypt, known for its hotels, beaches, and proximity to popular diving and snorkeling sites.
|
E298855
|
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: Hadaba | Statement: [Sharm El Sheikh, hasDistrict, Hadaba]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hadaba Context triple: [Sharm El Sheikh, hasDistrict, Hadaba]
-
A.
Haya
Haya is a feminine given name of Arabic origin, commonly used in the Middle East and among Arabic-speaking communities.
-
B.
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.
-
C.
Hana
Hana is a person known primarily as the romantic partner of Kip.
-
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.
Miyabi
Miyabi is a traditional Japanese-inspired lighting theme used on Tokyo Skytree, characterized by elegant, refined color schemes that evoke classical aesthetics.
- 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: Hadaba Triple: [Sharm El Sheikh, hasDistrict, Hadaba]
Generated description
Hadaba is a coastal district of Sharm El Sheikh in Egypt, known for its hotels, beaches, and proximity to popular diving and snorkeling sites.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hadaba Target entity description: Hadaba is a coastal district of Sharm El Sheikh in Egypt, known for its hotels, beaches, and proximity to popular diving and snorkeling sites.
-
A.
Haya
Haya is a feminine given name of Arabic origin, commonly used in the Middle East and among Arabic-speaking communities.
-
B.
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.
-
C.
Hana
Hana is a person known primarily as the romantic partner of Kip.
-
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.
Miyabi
Miyabi is a traditional Japanese-inspired lighting theme used on Tokyo Skytree, characterized by elegant, refined color schemes that evoke classical aesthetics.
- 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_69ab4b7f51d881908768300ebd2fbdae |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abddb3d63c8190b3ab5fa363c69db8 |
completed | March 7, 2026, 8:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afc6589558819088442f09db328dac |
completed | March 10, 2026, 7:20 a.m. |
| NEDg | Description generation | batch_69afc6d485d88190b281abec460ff24d |
completed | March 10, 2026, 7:23 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69afc750fee08190bf3c112f6f204af4 |
completed | March 10, 2026, 7:25 a.m. |
Created at: March 6, 2026, 9:58 p.m.