Triple
T9183523
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maximinus Daia |
E220393
|
entity |
| Predicate | deathPlace |
P21
|
FINISHED |
| Object | Tarsus |
E47685
|
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: Tarsus | Statement: [Maximinus Daia, deathPlace, Tarsus]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tarsus Context triple: [Maximinus Daia, deathPlace, Tarsus]
-
A.
Tarsus
chosen
Tarsus is an ancient city in Cilicia (in modern-day Turkey) known as a major cultural and commercial center of the Roman Empire and as the birthplace of the Apostle Paul.
-
B.
Smyrna
Smyrna was a major Aegean port city (modern-day İzmir in Turkey) that served as a key commercial and cultural hub in the Ottoman Empire.
-
C.
Smyrna
Smyrna is a suburban city in the Atlanta metropolitan area known for its residential communities, parks, and proximity to major Atlanta attractions.
-
D.
Smyrna
Smyrna is a small historic town in central Delaware known for its colonial-era roots and location along major transportation routes.
-
E.
Edessa
Edessa is a historic city in northern Greece renowned for its picturesque waterfalls and ancient heritage.
- 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_69ca83e6d77c81909862b7afef56b1bf |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccc31734908190b8ebe3b57849ca5f |
completed | April 1, 2026, 7:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d05c1474a08190aa3e3e90326a20d1 |
completed | April 4, 2026, 12:32 a.m. |
Created at: March 30, 2026, 7:24 p.m.