Triple
T5302353
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vinci |
E120011
|
entity |
| Predicate | hasNativeName |
P1435
|
FINISHED |
| Object | Vinci |
E120011
|
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: Vinci | Statement: [Vinci, hasNativeName, Vinci]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vinci Context triple: [Vinci, hasNativeName, Vinci]
-
A.
Vinci
chosen
Vinci is a small Tuscan town in Italy best known as the birthplace of Renaissance polymath Leonardo da Vinci.
-
B.
Vinci
Vinci is a major French concessions and construction company and one of the largest infrastructure and engineering groups in the world.
-
C.
Montech
Montech is a small commune in southern France known for its historic canal infrastructure and rural charm within the Tarn-et-Garonne department.
-
D.
Ferrand
Ferrand is a given name and surname of French origin, related to the name Ferdinand.
-
E.
Leonardo
Leonardo is the first name of Leonardo DiCaprio, the acclaimed American actor and environmental activist known for films such as Titanic and Inception.
- 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_69bd44704be88190acdb2ac481b0ff55 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd851a42f08190a3017050c136b747 |
completed | March 20, 2026, 5:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf21b22be08190ad5d3d6b12b80bcb |
completed | March 21, 2026, 10:54 p.m. |
Created at: March 20, 2026, 1:53 p.m.