Triple
T21624647
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jake Tapper |
E533666
|
entity |
| Predicate | twitterHandle |
P2866
|
FINISHED |
| Object |
@jaketapper
@jaketapper is the Twitter handle of Jake Tapper, a prominent American journalist and anchor for CNN.
|
E1494547
|
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: @jaketapper | Statement: [Jake Tapper, twitterHandle, @jaketapper]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: @jaketapper Context triple: [Jake Tapper, twitterHandle, @jaketapper]
-
A.
@jennafischer
@jennafischer is the official Twitter account of American actress Jenna Fischer, best known for her role as Pam Beesly on the U.S. version of "The Office."
-
B.
Taki
Taki is a small border town in West Bengal, India, situated along the Ichamati River opposite Bangladesh and known for its scenic riverfront and cultural significance.
-
C.
Taki
Taki is a Greek-born journalist and socialite known for his long-running, often controversial columns in publications like The Spectator.
-
D.
Taki
Taki was the wife of the renowned Japanese haiku poet Kobayashi Issa, sharing in the hardships and domestic life that shaped much of his later poetry.
-
E.
Jakes
Jakes is the surname of American author John Jakes, best known for his historical fiction sagas such as the "North and South" trilogy.
- 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: @jaketapper Triple: [Jake Tapper, twitterHandle, @jaketapper]
Generated description
@jaketapper is the Twitter handle of Jake Tapper, a prominent American journalist and anchor for CNN.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: @jaketapper Target entity description: @jaketapper is the Twitter handle of Jake Tapper, a prominent American journalist and anchor for CNN.
-
A.
@jennafischer
@jennafischer is the official Twitter account of American actress Jenna Fischer, best known for her role as Pam Beesly on the U.S. version of "The Office."
-
B.
Taki
Taki is a small border town in West Bengal, India, situated along the Ichamati River opposite Bangladesh and known for its scenic riverfront and cultural significance.
-
C.
Taki
Taki is a Greek-born journalist and socialite known for his long-running, often controversial columns in publications like The Spectator.
-
D.
Taki
Taki was the wife of the renowned Japanese haiku poet Kobayashi Issa, sharing in the hardships and domestic life that shaped much of his later poetry.
-
E.
Jakes
Jakes is the surname of American author John Jakes, best known for his historical fiction sagas such as the "North and South" trilogy.
- 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_69e0c464fba881908d0ff2ac80511ce1 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef52121c3c8190b9b4d862ed247c71 |
completed | April 27, 2026, 12:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0a0f92ff3881908f6319ef93abf078 |
completed | May 17, 2026, 6:57 p.m. |
| NEDg | Description generation | batch_6a0a10106ab4819096045effbbdfe95e |
completed | May 17, 2026, 6:59 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0a10c8c5bc8190add0ee63eb338964 |
completed | May 17, 2026, 7:02 p.m. |
Created at: April 16, 2026, 6:34 p.m.