Triple
T4430679
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Muret |
E95319
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Borbona |
E344552
|
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: Borbona | Statement: [Muret, hasTwinTown, Borbona]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Borbona Context triple: [Muret, hasTwinTown, Borbona]
-
A.
Borbona
chosen
Borbona is a small Italian town and comune in the Lazio region, known for its rural setting in the Apennine mountains and traditional local culture.
-
B.
Carlota
Carlota is the feminine given name corresponding to Carlos, commonly used in Spanish- and Portuguese-speaking cultures.
-
C.
Francisca
Francisca is a feminine given name, used in various European and Latin American cultures, that is cognate with the English name Frances.
-
D.
Isabela
Isabela is a large agricultural province in the Cagayan Valley region of the Philippines, known especially for its extensive rice and corn production.
-
E.
María Isabel
María Isabel is the birth name of Spanish actress Maribel Verdú, known for her prominent roles in films such as "Y Tu Mamá También" and "Pan's Labyrinth."
- 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_69b3453c2a0c8190926b574c90766db9 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35569b3388190bdef2568f5dc04ce |
completed | March 13, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b6136f350c81908d00eff850951ab0 |
completed | March 15, 2026, 2:03 a.m. |
Created at: March 12, 2026, 11:31 p.m.