Triple
T1836474
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Riyadh Province |
E41077
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Afif |
E205235
|
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: Afif | Statement: [Riyadh Province, hasCity, Afif]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Afif Context triple: [Riyadh Province, hasCity, Afif]
-
A.
Afif
chosen
Afif is a town in central Saudi Arabia known as an inland community within the Riyadh administrative region.
-
B.
Anif
Anif is a small Austrian municipality near Salzburg, known for its historic castle and as a residence of notable figures.
-
C.
Nafe
Nafe is an indigenous Oceanic language spoken in Vanuatu.
-
D.
Azara
Azara is a suburban locality on the outskirts of Guwahati in Assam, India, known for hosting the city's main international airport and related transport infrastructure.
-
E.
Alif
Alif is one of the official mascots of Expo 2020 Dubai, represented as a futuristic robot embodying innovation and mobility.
- 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_69a88647f9388190909bc36e795bdaec |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb028226481908558c11449e1d6b6 |
completed | March 7, 2026, 4:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69add1c23f9c81909b7d524448351060 |
completed | March 8, 2026, 7:45 p.m. |
Created at: March 4, 2026, 7:33 p.m.