Triple
T11990579
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miss Alma Peregrine |
E285393
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Alma |
E462179
|
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: Alma | Statement: [Miss Alma Peregrine, givenName, Alma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alma Context triple: [Miss Alma Peregrine, givenName, Alma]
-
A.
Alma
Alma is a historic wooden scow schooner preserved as a museum ship in San Francisco, representing the city’s 19th- and early 20th-century maritime commerce.
-
B.
Alma
Alma is a historic British Army battle honour commemorating the Battle of the Alma in the Crimean War.
-
C.
Alma
chosen
Alma is a feminine given name of Latin origin meaning "nourishing" or "kind," used in various cultures around the world.
-
D.
Alma
Alma is a small industrial and service city in Quebec, Canada, located in the Saguenay–Lac-Saint-Jean region and known for its aluminum production and proximity to Lac Saint-Jean.
-
E.
Alma Jr.
Alma Jr. is a fictional character from the film and short story "Brokeback Mountain," the daughter of Ennis Del Mar and Alma Beers Del Mar.
- 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_69d6ab44a77c8190a652f4b27164e4ef |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d903afe4388190a2cf2328e85adf9b |
completed | April 10, 2026, 2:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f47257911481909d6bd72535eefdbd |
completed | May 1, 2026, 9:28 a.m. |
Created at: April 8, 2026, 9:46 p.m.