Triple
T10898880
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shammar |
E257381
|
entity |
| Predicate | historicalRegion |
P915
|
FINISHED |
| Object | Najd |
E44274
|
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: Najd | Statement: [Shammar, historicalRegion, Najd]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Najd Context triple: [Shammar, historicalRegion, Najd]
-
A.
Najd
chosen
Najd is the central plateau region of Saudi Arabia, historically known as a heartland of Arab tribal culture and the birthplace of the modern Saudi state.
-
B.
Zau
Zau is an ancient city, historically known as Sais, that served as an important religious and political center in Egypt’s Nile Delta.
-
C.
Tayshet
Tayshet is a town in Irkutsk Oblast, Russia, known as a major railway junction in Siberia.
-
D.
Bayanhot
Bayanhot is the administrative center and main urban settlement of Alxa League in western Inner Mongolia, China.
-
E.
Nadym
Nadym is a town in the Yamalo-Nenets Autonomous Okrug of Russia, known as a regional center for the natural gas industry and served by its own airport.
- 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_69d6aa8550c8819095508a2ed9acf3db |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d761a2392c8190bc2c2359d63eff7a |
completed | April 9, 2026, 8:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e23baecb008190b74bcbad1db61a91 |
completed | April 17, 2026, 1:54 p.m. |
Created at: April 8, 2026, 9:21 p.m.