Triple
T15606520
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pinturicchio |
E375172
|
entity |
| Predicate | workLocation |
P7
|
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: [Pinturicchio, workLocation, Siena]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Siena Context triple: [Pinturicchio, workLocation, 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.
SIENA
SIENA is a secure communication platform used primarily by European law enforcement agencies to exchange sensitive information and coordinate cross-border operations.
-
C.
San Gimignano
San Gimignano is a medieval hill town in Tuscany, Italy, renowned for its well-preserved tower houses and historic cityscape.
-
D.
Pistoia
Pistoia is a historic Italian city known for its medieval architecture, vibrant cultural heritage, and location in the northern part of Tuscany.
-
E.
San Miniato
San Miniato is a historic hilltop town in Tuscany, Italy, known for its medieval architecture and prized white truffles.
- 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_69d85ccf2794819096cda4cbcb02d478 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e7ec08c8190b3842cf3043aea27 |
completed | April 16, 2026, 2:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff75655c948190aa1afa424e5270d1 |
completed | May 9, 2026, 5:56 p.m. |
Created at: April 10, 2026, 4:13 a.m.