Triple
T2341882
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cyd Charisse |
E45043
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Tula
Tula is the birth name of American actress and dancer Cyd Charisse, famed for her roles in classic Hollywood musicals.
|
E258799
|
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: Tula | Statement: [Cyd Charisse, givenName, Tula]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tula Context triple: [Cyd Charisse, givenName, Tula]
-
A.
Tula
Tula is a historic Russian city south of Moscow, known for its metalworking, samovar production, and as a cultural center near Leo Tolstoy’s estate at Yasnaya Polyana.
-
B.
Tula
Tula is an important ancient Mesoamerican city, once a major Toltec capital known for its monumental architecture and iconic stone warrior statues.
-
C.
Sabinas
Sabinas is a municipality and city in the northern Mexican state of Coahuila, known historically for its coal mining and regional agricultural activities.
-
D.
Apodaca
Apodaca is a rapidly growing industrial city and suburb of Monterrey in the Mexican state of Nuevo León.
-
E.
Ciudad Valles
Ciudad Valles is a key urban and commercial center in the Huasteca region of northeastern Mexico, known as a gateway to the waterfalls, rivers, and ecotourism attractions of eastern San Luis Potosí.
- 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: Tula Triple: [Cyd Charisse, givenName, Tula]
Generated description
Tula is the birth name of American actress and dancer Cyd Charisse, famed for her roles in classic Hollywood musicals.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tula Target entity description: Tula is the birth name of American actress and dancer Cyd Charisse, famed for her roles in classic Hollywood musicals.
-
A.
Tula
Tula is a historic Russian city south of Moscow, known for its metalworking, samovar production, and as a cultural center near Leo Tolstoy’s estate at Yasnaya Polyana.
-
B.
Tula
Tula is an important ancient Mesoamerican city, once a major Toltec capital known for its monumental architecture and iconic stone warrior statues.
-
C.
Sabinas
Sabinas is a municipality and city in the northern Mexican state of Coahuila, known historically for its coal mining and regional agricultural activities.
-
D.
Apodaca
Apodaca is a rapidly growing industrial city and suburb of Monterrey in the Mexican state of Nuevo León.
-
E.
Ciudad Valles
Ciudad Valles is a key urban and commercial center in the Huasteca region of northeastern Mexico, known as a gateway to the waterfalls, rivers, and ecotourism attractions of eastern San Luis Potosí.
- 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_69a88917935081909b755dbf38e81024 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abc6ad01fc81909e386986e9acc989 |
completed | March 7, 2026, 6:33 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae9622cdb08190835222482bd22cf4 |
completed | March 9, 2026, 9:42 a.m. |
| NEDg | Description generation | batch_69ae977776ec8190ad5f7ce4594d73d9 |
completed | March 9, 2026, 9:48 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae9b64daf08190afa6898242bde864 |
completed | March 9, 2026, 10:05 a.m. |
Created at: March 4, 2026, 7:52 p.m.