Triple
T4310908
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Po |
E94070
|
entity |
| Predicate | flowsNearCity |
P3945
|
FINISHED |
| Object | Mantua |
E68502
|
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: Mantua | Statement: [Po, flowsNearCity, Mantua]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mantua Context triple: [Po, flowsNearCity, Mantua]
-
A.
Mantua
chosen
Mantua is a historic city in northern Italy’s Lombardy region, renowned for its Renaissance architecture, artistic heritage, and former status as the seat of the Gonzaga dynasty.
-
B.
Mantua
Mantua is a residential neighborhood in West Philadelphia known for its historic rowhouses and proximity to major universities and cultural institutions.
-
C.
Mantua
Mantua is a small Cuban town and municipality located in the western part of Pinar del Río Province.
-
D.
Verona
Verona is a historic city in northern Italy renowned for its well-preserved Roman architecture and its association with Shakespeare’s "Romeo and Juliet."
-
E.
Cremona
Cremona is a historic city in northern Italy renowned for its tradition of violin making and its well-preserved medieval architecture.
- 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_69b3451886588190a3dd1305ea7c58dc |
completed | March 12, 2026, 10:58 p.m. |
| NER | Named-entity recognition | batch_69b350d6cba08190a9440cee7b9dc40d |
completed | March 12, 2026, 11:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b613464adc8190bc82f599d30d7b13 |
completed | March 15, 2026, 2:02 a.m. |
Created at: March 12, 2026, 11:11 p.m.