Triple
T6373772
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Volterra |
E143412
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Siena |
E168770
|
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: Siena | Statement: [Volterra, locatedNear, Siena]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Siena Context triple: [Volterra, locatedNear, Siena]
-
A.
Siena
chosen
Siena is a historic Tuscan city renowned for its medieval brick architecture, fan-shaped Piazza del Campo, and the Palio horse race.
-
B.
San Gimignano
San Gimignano is a medieval hill town in Tuscany, Italy, renowned for its well-preserved tower houses and historic cityscape.
-
C.
Pistoia
Pistoia is a historic Italian city known for its medieval architecture, vibrant cultural heritage, and location in the northern part of Tuscany.
-
D.
San Miniato
San Miniato is a historic hilltop town in Tuscany, Italy, known for its medieval architecture and prized white truffles.
-
E.
Arezzo
Arezzo is an ancient Tuscan city in central Italy, historically significant as one of the principal centers of the Etruscan civilization and later a prominent medieval and Renaissance town.
- 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_69c008d9f4348190ab598a2913259a1c |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c068390af08190967a3d25ec9a50e5 |
completed | March 22, 2026, 10:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c64badc3c481908199bf32069cc1c7 |
completed | March 27, 2026, 9:19 a.m. |
Created at: March 22, 2026, 4:33 p.m.