Triple
T15284859
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hurricane Camille |
E365366
|
entity |
| Predicate | replacedByName |
P3432
|
FINISHED |
| Object | Carmen |
E358979
|
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: Carmen | Statement: [Hurricane Camille, replacedByName, Carmen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Carmen Context triple: [Hurricane Camille, replacedByName, Carmen]
-
A.
Carmen
Carmen is a landlocked agricultural municipality in the province of North Cotabato on the island of Mindanao in the Philippines.
-
B.
Carmen
chosen
Carmen is a feminine given name of Latin origin, widely used in Spanish-speaking cultures and beyond.
-
C.
Carmen
Carmen is a pivotal character in the 1986 film "The Color of Money," serving as the savvy and manipulative girlfriend-manager of young pool hustler Vincent Lauria.
-
D.
Carmen
Carmen is a character in the independent crime drama film "Dinner Rush," which centers on the high-pressure world of a New York City restaurant.
-
E.
Carmen
Carmen is the central protagonist of the story "Abracadabra," around whom the plot and character dynamics revolve.
- 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_69d85a103d9081908c1ea6c4c73ac8e3 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e00e53c9588190a6cb61ac8805c706 |
completed | April 15, 2026, 10:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69feef798a588190981c77e6f4c6be78 |
completed | May 9, 2026, 8:25 a.m. |
Created at: April 10, 2026, 3:15 a.m.