Triple
T9970972
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Betty Garde |
E196201
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Garde |
E196201
|
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: Garde | Statement: [Betty Garde, familyName, Garde]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Garde Context triple: [Betty Garde, familyName, Garde]
-
A.
Garde
chosen
Garde is the surname of American stage, film, and radio actress Betty Garde, known for her character roles in mid-20th-century entertainment.
-
B.
Gardez
Gardez is a city in eastern Afghanistan that serves as the capital of Paktia Province and an important regional administrative and commercial center.
-
C.
Garding
Garding is a small town in the Nordfriesland district of Schleswig-Holstein in northern Germany.
-
D.
Guardea
Guardea is a small Italian town and comune in the Umbria region, known for its medieval historic center and scenic position overlooking the Tiber Valley.
-
E.
Gardein
Gardein is a plant-based food brand known for its wide range of meatless products such as chicken, beef, and fish alternatives made from soy, wheat, and pea proteins.
- 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_69ca82eea2b88190a0e511d21a31f386 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb7b96b1c8190b9d3c1171346615a |
completed | April 2, 2026, 12:26 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d23dca14d081909573e91a576921c9 |
completed | April 5, 2026, 10:47 a.m. |
Created at: March 30, 2026, 8:48 p.m.