Triple
T9298869
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blanca Rosa Welter |
E223708
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Welter |
E223710
|
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: Welter | Statement: [Blanca Rosa Welter, familyName, Welter]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Welter Context triple: [Blanca Rosa Welter, familyName, Welter]
-
A.
Welter
chosen
Welter is the surname of Mexican film actress Linda Christian, known as the first "Bond girl" in the 1954 television adaptation of Casino Royale.
-
B.
Wyoma
Wyoma is a residential neighborhood located within the city of Lynn in northeastern Massachusetts.
-
C.
Wala
The Wala are an ethnic group primarily inhabiting the Upper West Region of Ghana, known for their Islamic heritage, centralized chieftaincy system, and rich traditions in trade and craftsmanship.
-
D.
Wala
Wala is an Oceanic language variety spoken in Vanuatu, recognized as one of the dialects within the Uripiv-Wala-Rano-Atchin dialect chain.
-
E.
Wasmer
Wasmer is a WebAssembly runtime that enables running WebAssembly modules efficiently across different platforms and programming languages.
- 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_69ca8423edb08190bc0c91287a484768 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd08cf50cc8190a025f478dff4f9fd |
completed | April 1, 2026, noon |
| NED1 | Entity disambiguation (via context triple) | batch_69d0b25ac97881908751b466b370b7e5 |
completed | April 4, 2026, 6:40 a.m. |
Created at: March 30, 2026, 7:36 p.m.