Triple
T625536
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Weinstein Company |
E15809
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Carol |
E110920
|
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: Carol | Statement: [The Weinstein Company, notableWork, Carol]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Carol Context triple: [The Weinstein Company, notableWork, Carol]
-
A.
Carol
chosen
Carol is a feminine given name commonly used in English-speaking countries, often associated with figures in entertainment and literature.
-
B.
Barbara
Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
-
C.
Nancy
Nancy is a feminine given name of Hebrew origin meaning "grace" that became especially popular in English-speaking countries in the 20th century.
-
D.
Nancy
Nancy is a historic city in northeastern France renowned for its elegant 18th-century architecture and UNESCO-listed Place Stanislas.
-
E.
Carla
Carla is a feminine given name commonly used in various languages, often considered the female form of Carl or Charles.
- 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_69a4935c131c8190a5378c6bf101e8cc |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a49e574444819087999404f3e3ffd9 |
completed | March 1, 2026, 8:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac1185da8c8190a70f4fd885bbe209 |
completed | March 7, 2026, 11:52 a.m. |
Created at: March 1, 2026, 7:35 p.m.