Triple
T19817070
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Talal bin Abdulaziz Al Saud |
E476086
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Taif |
—
|
NE NERFINISHED |
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: Taif | Statement: [Talal bin Abdulaziz Al Saud, placeOfBirth, Taif]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Taif Context triple: [Talal bin Abdulaziz Al Saud, placeOfBirth, Taif]
-
A.
Taif
chosen
Taif is a city in western Saudi Arabia known for its cool climate, rose cultivation, and historical significance as a summer resort and cultural center.
-
B.
Tajuan
Tajuan is the given first name of former NFL cornerback Ty Law.
-
C.
Wangtu
Wangtu is a small settlement in the Kinnaur district of Himachal Pradesh, India, situated along the Sutlej River in the Himalayan region.
-
D.
Taketa
Taketa is a small historic city in Japan known for its scenic rural landscapes, hot springs, and castle ruins.
-
E.
Aiga-i-le-Tai
Aiga-i-le-Tai is a rural district on the island of Upolu in Samoa that includes coastal villages and the country’s main international airport.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e51bc4208190a1c57d8c5d1b15e4 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e654f9c5b08190987237f5144c3b37 |
completed | April 20, 2026, 4:31 p.m. |
Created at: April 10, 2026, 1:50 p.m.