Triple
T876937
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mário Soares |
E18938
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Mário |
E95100
|
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: Mário | Statement: [Mário Soares, givenName, Mário]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mário Context triple: [Mário Soares, givenName, Mário]
-
A.
Mario
chosen
Mario is an American R&B singer, songwriter, and occasional actor best known for his early-2000s hits like "Let Me Love You."
-
B.
Mario
Mario is a fictional Italian plumber and the iconic protagonist of Nintendo's long-running Super Mario video game franchise.
-
C.
Yoshi
Yoshi is a friendly, dinosaur-like character from Nintendo’s Mario franchise, known for his long tongue, egg-throwing abilities, and frequent role as Mario’s companion and steed.
-
D.
Luigi
Luigi is a timid yet heroic green-clad plumber from Nintendo’s Mario franchise, known as Mario’s younger brother and frequent co-adventurer.
-
E.
Taitō
Taitō is a special ward in central Tokyo known for its historic districts, traditional temples, and major cultural attractions such as Ueno Park and Asakusa.
- 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_69a4938db1f081909bcd1ad2713b6096 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4acaf30a48190a10ed7fee464c444 |
completed | March 1, 2026, 9:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7b854469c81908dc4a5c29ee140a4 |
completed | March 4, 2026, 4:43 a.m. |
Created at: March 1, 2026, 7:39 p.m.