Triple
T2497644
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Negev desert |
E52188
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Mamshit
Mamshit is an ancient Nabatean trading city and UNESCO World Heritage archaeological site located in Israel’s Negev desert.
|
E271717
|
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: Mamshit | Statement: [Negev desert, hasCity, Mamshit]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mamshit Context triple: [Negev desert, hasCity, Mamshit]
-
A.
Baby-G
Baby-G is a line of durable, fashion-oriented digital watches designed by Casio, often featuring shock resistance, water resistance, and colorful, youth-focused styling.
-
B.
Bebek
Bebek is an upscale seaside neighborhood on Istanbul’s Bosphorus shore, known for its scenic views, cafes, and vibrant social life.
-
C.
Liddy
Liddy is a surname most notably associated with G. Gordon Liddy, the former FBI agent and key figure in the Watergate scandal.
-
D.
Bündchen
Bündchen is the German-origin surname most famously borne by Brazilian supermodel Gisele Bündchen.
-
E.
Mam
Mam is a Mayan language spoken primarily by the Mam people in the western highlands of Guatemala and parts of southern Mexico.
- 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: Mamshit Triple: [Negev desert, hasCity, Mamshit]
Generated description
Mamshit is an ancient Nabatean trading city and UNESCO World Heritage archaeological site located in Israel’s Negev desert.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mamshit Target entity description: Mamshit is an ancient Nabatean trading city and UNESCO World Heritage archaeological site located in Israel’s Negev desert.
-
A.
Baby-G
Baby-G is a line of durable, fashion-oriented digital watches designed by Casio, often featuring shock resistance, water resistance, and colorful, youth-focused styling.
-
B.
Bebek
Bebek is an upscale seaside neighborhood on Istanbul’s Bosphorus shore, known for its scenic views, cafes, and vibrant social life.
-
C.
Liddy
Liddy is a surname most notably associated with G. Gordon Liddy, the former FBI agent and key figure in the Watergate scandal.
-
D.
Bündchen
Bündchen is the German-origin surname most famously borne by Brazilian supermodel Gisele Bündchen.
-
E.
Mam
Mam is a Mayan language spoken primarily by the Mam people in the western highlands of Guatemala and parts of southern Mexico.
- 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_69ab4955111c8190835bf619adec21ff |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd1ad2f8c81908853e97d75081e84 |
completed | March 7, 2026, 7:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af1f9be594819099a03a2784691124 |
completed | March 9, 2026, 7:29 p.m. |
| NEDg | Description generation | batch_69af200e2db4819085851a45213edc89 |
completed | March 9, 2026, 7:31 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69af208dfab081909d706aad8ff5f615 |
completed | March 9, 2026, 7:33 p.m. |
Created at: March 6, 2026, 9:46 p.m.