Triple
T4928463
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frank Lautenberg |
E110633
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Frank
Frank is the given name of Frank Lautenberg, a long-serving United States Senator from New Jersey known for his work on public health and transportation safety.
|
E480928
|
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: Frank | Statement: [Frank Lautenberg, givenName, Frank]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Frank Context triple: [Frank Lautenberg, givenName, Frank]
-
A.
Frank
Frank is the given name of the American painter, sculptor, and printmaker Frank Stella, a leading figure in minimalism and post-painterly abstraction.
-
B.
Frank
Frank is the given name of the renowned Canadian-American architect Frank Gehry, celebrated for his deconstructivist and sculptural building designs.
-
C.
Frank
Frank is a key supporting character in the post-apocalyptic horror film "28 Days Later," known as a protective father trying to keep his daughter safe amid a devastating viral outbreak in London.
-
D.
Frank
Frank is the Allied reporting name for the Japanese Nakajima Ki-84, a highly capable World War II fighter aircraft used by the Imperial Japanese Army Air Service.
-
E.
Frank
Frank is the given name of Frank Abagnale Jr., the infamous former con artist whose life inspired the film "Catch Me If You Can."
- 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: Frank Triple: [Frank Lautenberg, givenName, Frank]
Generated description
Frank is the given name of Frank Lautenberg, a long-serving United States Senator from New Jersey known for his work on public health and transportation safety.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Frank Target entity description: Frank is the given name of Frank Lautenberg, a long-serving United States Senator from New Jersey known for his work on public health and transportation safety.
-
A.
Frank
Frank is the given name of Frank B. Kellogg, an American lawyer, diplomat, and Nobel Peace Prize–winning U.S. Secretary of State.
-
B.
Frank
Frank is the given name of Frank Oz, the renowned puppeteer, actor, and director best known for his work with the Muppets and on Star Wars.
-
C.
Frank
Frank is the given name of Frank Lampard, the renowned English former professional footballer and manager.
-
D.
Frank
Frank is the given name of the renowned Canadian-American architect Frank Gehry, celebrated for his deconstructivist and sculptural building designs.
-
E.
Frank
Frank is a common surname of Germanic origin borne by numerous notable individuals across politics, arts, and other fields.
- 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_69bd4415190c8190817bee7ec9f9f944 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd7038c12c81908a793b4a8768c28a |
completed | March 20, 2026, 4:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be77ac88148190a51fa2e9085d6897 |
completed | March 21, 2026, 10:49 a.m. |
| NEDg | Description generation | batch_69be7847a378819081687ec783a8b862 |
completed | March 21, 2026, 10:51 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69be7915a26c81909b21a128daebf5b3 |
completed | March 21, 2026, 10:55 a.m. |
Created at: March 20, 2026, 1:30 p.m.