Triple
T11949973
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pippa Passes |
E284400
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Pippa
Pippa is the innocent, optimistic young girl at the heart of Robert Browning’s verse drama "Pippa Passes," whose unknowing influence shapes the lives of others.
|
E956721
|
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: Pippa | Statement: [Pippa Passes, mainCharacter, Pippa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pippa Context triple: [Pippa Passes, mainCharacter, Pippa]
-
A.
Pippa
Pippa is an English socialite, author, and columnist best known as the younger sister of Catherine, Princess of Wales.
-
B.
Jemma Jupe
Jemma Jupe is a member of the Jupe family, known publicly as a relative of English actor Noah Jupe.
-
C.
Penelope Horner
Penelope Horner was a British actress active in film and television during the mid-20th century, known for her supporting roles in comedies and dramas.
-
D.
Tamsin
Tamsin is a feminine given name of English origin, often associated with actresses and public figures such as Tamsin Egerton.
-
E.
Tessa Quayle
Tessa Quayle is a passionate human-rights activist whose mysterious death in Kenya drives the political and emotional intrigue at the heart of John le Carré’s novel "The Constant Gardener."
- 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: Pippa Triple: [Pippa Passes, mainCharacter, Pippa]
Generated description
Pippa is the innocent, optimistic young girl at the heart of Robert Browning’s verse drama "Pippa Passes," whose unknowing influence shapes the lives of others.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Pippa Target entity description: Pippa is the innocent, optimistic young girl at the heart of Robert Browning’s verse drama "Pippa Passes," whose unknowing influence shapes the lives of others.
-
A.
Pippa
Pippa is an English socialite, author, and columnist best known as the younger sister of Catherine, Princess of Wales.
-
B.
Jemma Jupe
Jemma Jupe is a member of the Jupe family, known publicly as a relative of English actor Noah Jupe.
-
C.
Penelope Horner
Penelope Horner was a British actress active in film and television during the mid-20th century, known for her supporting roles in comedies and dramas.
-
D.
Tamsin
Tamsin is a feminine given name of English origin, often associated with actresses and public figures such as Tamsin Egerton.
-
E.
Tessa Quayle
Tessa Quayle is a passionate human-rights activist whose mysterious death in Kenya drives the political and emotional intrigue at the heart of John le Carré’s novel "The Constant Gardener."
- 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_69d6ab2db38c8190b1f0ed6663ef8ada |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d90364c2608190a3946c9595c71164 |
completed | April 10, 2026, 2:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f458f16f088190a0005ff0fd4f547f |
completed | May 1, 2026, 7:40 a.m. |
| NEDg | Description generation | batch_69f45f86349c81909a806fd7be4008e9 |
completed | May 1, 2026, 8:08 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f4647ee1748190975bce3bbf51a3bc |
completed | May 1, 2026, 8:29 a.m. |
Created at: April 8, 2026, 9:45 p.m.