Triple
T7101873
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | María Kodama |
E165477
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Kodama |
E219821
|
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: Kodama | Statement: [María Kodama, familyName, Kodama]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kodama Context triple: [María Kodama, familyName, Kodama]
-
A.
Kodama
chosen
Kodama is a Japanese surname borne by various notable figures in fields such as politics, the military, the arts, and sports.
-
B.
Kodama
Kodama is a Japanese Shinkansen train service known for its all-stop, slower-speed runs along high-speed rail lines such as the Tokaido Shinkansen.
-
C.
Moruya
Moruya is a coastal town in New South Wales, Australia, known for its scenic river setting, nearby beaches, and historic granite quarries.
-
D.
Yokadouma
Yokadouma is a town in eastern Cameroon that serves as an important local administrative and commercial center near the country's forested border regions.
-
E.
Asagumo
Asagumo was a Japanese destroyer of the Imperial Japanese Navy that saw action in World War II, including participation in major Pacific naval engagements.
- 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_69c6887fcddc8190a5d58908f6dee590 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e588aee08190bfb3d96135c0a322 |
completed | March 27, 2026, 8:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c82754e9248190b86b05b61a4ae23c |
completed | March 28, 2026, 7:09 p.m. |
Created at: March 27, 2026, 2:42 p.m.