Triple
T1275334
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Masaa gallery for Sa'i |
E27199
|
entity |
| Predicate | connects |
P390
|
FINISHED |
| Object | Safa |
E28291
|
NE FINISHED |
How this triple was built (2 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: Safa | Statement: [Masaa gallery for Sa'i, connects, Safa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Safa Context triple: [Masaa gallery for Sa'i, connects, Safa]
-
A.
al-Safa
chosen
Al-Safa is one of the two small hills inside the Masjid al-Haram in Mecca between which Muslims perform the ritual walk (sa'i) during Hajj and Umrah.
-
B.
Nasar
Nasar is a surname most notably associated with Sylvia Nasar, the economist and author of "A Beautiful Mind."
-
C.
Rutba
Rutba is a remote desert town in western Iraq that serves as a key transit point on the highway linking Baghdad with Jordan and Syria.
-
D.
Al Mahara
Al Mahara is a high-end seafood restaurant in Dubai renowned for its immersive floor-to-ceiling aquarium setting inside the iconic Burj Al Arab hotel.
-
E.
Masri
Masri is a widely spoken modern Arabic dialect used primarily in Egypt, especially in everyday conversation and popular media.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69a496d3710c8190955dee8bc0dacb50 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c06ee22081908141868b57596e35 |
completed | March 1, 2026, 10:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acacb7ee8c819084ef29881b2274cb |
completed | March 7, 2026, 10:54 p.m. |
Created at: March 1, 2026, 7:50 p.m.