Triple
T499780
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Qena |
E10374
|
entity |
| Predicate | nearbyCity |
P350
|
FINISHED |
| Object | Luxor |
E16540
|
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: Luxor | Statement: [Qena, nearbyCity, Luxor]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Luxor Context triple: [Qena, nearbyCity, Luxor]
-
A.
Luxor
chosen
Luxor is a historic city in southern Egypt renowned for its vast ancient temple complexes and royal tombs along the Nile.
-
B.
Helwan
Helwan is an industrial and residential city in southern Greater Cairo, Egypt, known for its factories, universities, and historic spa resorts along the Nile.
-
C.
Giza
Giza is an Egyptian city on the west bank of the Nile, famous for the Giza Plateau where the Great Pyramids and the Sphinx are located.
-
D.
Abydos
Abydos is an ancient Egyptian city renowned as a major religious center and burial site, closely associated with the cult of Osiris and other important deities.
-
E.
Hurghada
Hurghada is a major Egyptian Red Sea resort city known for its beaches, diving, and tourism industry.
- 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_69a2e847df8481909239ec08ccf1e376 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f13096248190a622a58dcf540b00 |
completed | Feb. 28, 2026, 1:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a5089817248190b53d78e5b937026b |
completed | March 2, 2026, 3:48 a.m. |
Created at: Feb. 28, 2026, 1:12 p.m.