Triple
T14475748
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | conquest of Tayma |
E358965
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object | Tayma |
E255451
|
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: Tayma | Statement: [conquest of Tayma, relatedTo, Tayma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tayma Context triple: [conquest of Tayma, relatedTo, Tayma]
-
A.
Tayma
chosen
Tayma is an ancient oasis town in northwestern Saudi Arabia known for its significant archaeological remains and long history as a caravan and trade center.
-
B.
Taybad
Taybad is a city in northeastern Iran near the Afghan border, known as a local commercial and transit hub within Razavi Khorasan Province.
-
C.
Tamahaq
Tamahaq is a Berber (Amazigh) language traditionally spoken by the Tuareg people of the central Sahara.
-
D.
Tayshet
Tayshet is a town in Irkutsk Oblast, Russia, known as a major railway junction in Siberia.
-
E.
Moura
Moura is a historic town in Portugal’s Alentejo region, known for its whitewashed architecture, olive oil production, and proximity to the Alqueva reservoir.
- 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_69d827966698819082e140837737501d |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de91fc1fc48190842b09aa03ba79f8 |
completed | April 14, 2026, 7:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd6d8ccd608190afd23c903cd5686a |
completed | May 8, 2026, 4:58 a.m. |
Created at: April 10, 2026, 1:20 a.m.