Triple
T6197780
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Herta Haas |
E138551
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Herta
Herta is a feminine given name of Germanic origin, commonly used in Central and Eastern Europe.
|
E574940
|
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: Herta | Statement: [Herta Haas, givenName, Herta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Herta Context triple: [Herta Haas, givenName, Herta]
-
A.
Stella Carlin
Stella Carlin is a rebellious and charismatic inmate character from the television series "Orange Is the New Black."
-
B.
Mercedes Barcha
Mercedes Barcha was the longtime wife and muse of Nobel Prize–winning author Gabriel García Márquez, known for her steadfast support throughout his literary career.
-
C.
Toni Krinner
Toni Krinner was a German ice hockey coach and former player known for his coaching roles in the Deutsche Eishockey Liga.
-
D.
Odile Speed
Odile Speed was a British artist and the wife of molecular biologist Francis Crick, noted for her role in the social and intellectual circles surrounding the discovery of DNA’s structure.
-
E.
Mercedes Tomasa
Mercedes Tomasa de San Martín was the daughter of Argentine independence leader General José de San Martín and is remembered primarily for her close association with his legacy.
- 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: Herta Triple: [Herta Haas, givenName, Herta]
Generated description
Herta is a feminine given name of Germanic origin, commonly used in Central and Eastern Europe.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Herta Target entity description: Herta is a feminine given name of Germanic origin, commonly used in Central and Eastern Europe.
-
A.
Stella Carlin
Stella Carlin is a rebellious and charismatic inmate character from the television series "Orange Is the New Black."
-
B.
Mercedes Barcha
Mercedes Barcha was the longtime wife and muse of Nobel Prize–winning author Gabriel García Márquez, known for her steadfast support throughout his literary career.
-
C.
Toni Krinner
Toni Krinner was a German ice hockey coach and former player known for his coaching roles in the Deutsche Eishockey Liga.
-
D.
Odile Speed
Odile Speed was a British artist and the wife of molecular biologist Francis Crick, noted for her role in the social and intellectual circles surrounding the discovery of DNA’s structure.
-
E.
Mercedes Tomasa
Mercedes Tomasa de San Martín was the daughter of Argentine independence leader General José de San Martín and is remembered primarily for her close association with his legacy.
- 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_69c008ab9b3081908a11b2c744838435 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c06251b47881909bd8d2ea37541959 |
completed | March 22, 2026, 9:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c16f2a40a88190847f607a6e2c5f4e |
completed | March 23, 2026, 4:49 p.m. |
| NEDg | Description generation | batch_69c1d232ab1881909cc3014beb664446 |
completed | March 23, 2026, 11:52 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c1d2c532988190a98f615638987159 |
completed | March 23, 2026, 11:54 p.m. |
Created at: March 22, 2026, 4:20 p.m.