Triple
T5694991
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Diane Venora |
E125516
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Venora
Venora is the surname of American actress Diane Venora, known for her work in film, television, and theater.
|
E541979
|
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: Venora | Statement: [Diane Venora, familyName, Venora]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Venora Context triple: [Diane Venora, familyName, Venora]
-
A.
Velda
Velda is the loyal and resourceful secretary and love interest of private investigator Mike Hammer in the hardboiled crime novel and film "Kiss Me Deadly."
-
B.
Virganskaya
Virganskaya is a Russian surname most notably borne by Irina Virganskaya, the daughter of former Soviet leader Mikhail Gorbachev.
-
C.
Vyartsilya
Vyartsilya is a small urban-type settlement in the Republic of Karelia, Russia, near the border with Finland.
-
D.
Novilara
Novilara is an archaeological site and locality in the Marche region of Italy, known for its ancient Picene culture remains and notable funerary stelae.
-
E.
Loralai
Loralai is a town and district in northern Balochistan, Pakistan, known historically as a regional administrative and trade center.
- 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: Venora Triple: [Diane Venora, familyName, Venora]
Generated description
Venora is the surname of American actress Diane Venora, known for her work in film, television, and theater.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Venora Target entity description: Venora is the surname of American actress Diane Venora, known for her work in film, television, and theater.
-
A.
Velda
Velda is the loyal and resourceful secretary and love interest of private investigator Mike Hammer in the hardboiled crime novel and film "Kiss Me Deadly."
-
B.
Virganskaya
Virganskaya is a Russian surname most notably borne by Irina Virganskaya, the daughter of former Soviet leader Mikhail Gorbachev.
-
C.
Vyartsilya
Vyartsilya is a small urban-type settlement in the Republic of Karelia, Russia, near the border with Finland.
-
D.
Novilara
Novilara is an archaeological site and locality in the Marche region of Italy, known for its ancient Picene culture remains and notable funerary stelae.
-
E.
Loralai
Loralai is a town and district in northern Balochistan, Pakistan, known historically as a regional administrative and trade center.
- 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_69c0082bb19c8190823a4facd3cba79b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c02409e70081909e47f2bd4a50fa12 |
completed | March 22, 2026, 5:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c05a528a348190a7f6fd4cc3b76c92 |
completed | March 22, 2026, 9:08 p.m. |
| NEDg | Description generation | batch_69c05d8890148190a4f81b2c1ca70886 |
completed | March 22, 2026, 9:22 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c0620ee1848190935f5f78abbed7ba |
completed | March 22, 2026, 9:41 p.m. |
Created at: March 22, 2026, 3:45 p.m.