Triple
T6848901
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nadine Gordimer |
E157964
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Nadine
Nadine is a feminine given name used in various cultures, often associated with the meaning "hope."
|
E624880
|
NE FINISHED |
How this triple was built (4 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: Nadine | Statement: [Nadine Gordimer, givenName, Nadine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nadine Context triple: [Nadine Gordimer, givenName, Nadine]
-
A.
Nadine
"Nadine" is a classic 1964 rock and roll song by Chuck Berry, known for its vivid storytelling and driving guitar riff.
-
B.
Sonia
Sonia is a central female character in the romantic comedy film "Think Like a Man," whose relationships and personal growth intersect with the movie’s ensemble cast and themes about modern dating.
-
C.
Sonia
Sonia is the given name of Sonia Gandhi, an Italian-born Indian politician and former president of the Indian National Congress.
-
D.
Natalie
Natalie is the central protagonist of the British film "Life Is Sweet," around whom the story’s family and everyday struggles revolve.
-
E.
Natalie
Natalie is a fictional character from the romantic comedy universe of "Love Actually," appearing in the charity sequel short film "Red Nose Day Actually."
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Nadine Triple: [Nadine Gordimer, givenName, Nadine]
Generated description
Nadine is a feminine given name used in various cultures, often associated with the meaning "hope."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nadine Target entity description: Nadine is a feminine given name used in various cultures, often associated with the meaning "hope."
-
A.
Nadine
"Nadine" is a classic 1964 rock and roll song by Chuck Berry, known for its vivid storytelling and driving guitar riff.
-
B.
Sonia
Sonia is a central female character in the romantic comedy film "Think Like a Man," whose relationships and personal growth intersect with the movie’s ensemble cast and themes about modern dating.
-
C.
Sonia
Sonia is the given name of Sonia Gandhi, an Italian-born Indian politician and former president of the Indian National Congress.
-
D.
Natalie
Natalie is the central protagonist of the science fiction thriller film "The Darkest Hour," around whom the story’s alien-invasion survival plot revolves.
-
E.
Natalie
Natalie is the central protagonist of the British film "Life Is Sweet," around whom the story’s family and everyday struggles revolve.
- F. None of above. chosen
Provenance (5 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_69c6882ed4c081909dc465a7cf8838be |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d84acb288190a67d974197ecab2f |
completed | March 27, 2026, 7:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7427825d881909f151ca2ce3bd546 |
completed | March 28, 2026, 2:52 a.m. |
| NEDg | Description generation | batch_69c7435af2b481908e06b3ec72dae7da |
completed | March 28, 2026, 2:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c7443919ec819089040e50462864d1 |
completed | March 28, 2026, 3 a.m. |
Created at: March 27, 2026, 2:20 p.m.