Triple
T21898997
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jenny Schecter |
E540757
|
entity |
| Predicate | hasPartner |
P1136
|
FINISHED |
| Object |
Marina Ferrer
Marina Ferrer is a charismatic, sophisticated book publisher and central love interest in the early seasons of the television series "The L Word."
|
E1508063
|
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: Marina Ferrer | Statement: [Jenny Schecter, hasPartner, Marina Ferrer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marina Ferrer Context triple: [Jenny Schecter, hasPartner, Marina Ferrer]
-
A.
Ana Valdés
Ana Valdés is a notable individual recognized for her contributions in her field, bearing the surname Valdés.
-
B.
Cala Ferrera
Cala Ferrera is a small, picturesque beach and cove on the southeast coast of Mallorca, Spain, known for its clear turquoise waters and family-friendly resort atmosphere.
-
C.
Emma Ferrer
Emma Ferrer is a Swiss-born artist, model, and humanitarian, best known as the granddaughter of iconic actress Audrey Hepburn.
-
D.
Blanca Parés
Blanca Parés is a Spanish actress best known for her role in Pedro Almodóvar’s acclaimed film "Julieta."
-
E.
Carmen Rabassa
Carmen Rabassa is known primarily as the wife of acclaimed American literary translator Gregory Rabassa.
- 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: Marina Ferrer Triple: [Jenny Schecter, hasPartner, Marina Ferrer]
Generated description
Marina Ferrer is a charismatic, sophisticated book publisher and central love interest in the early seasons of the television series "The L Word."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Marina Ferrer Target entity description: Marina Ferrer is a charismatic, sophisticated book publisher and central love interest in the early seasons of the television series "The L Word."
-
A.
Ana Valdés
Ana Valdés is a notable individual recognized for her contributions in her field, bearing the surname Valdés.
-
B.
Cala Ferrera
Cala Ferrera is a small, picturesque beach and cove on the southeast coast of Mallorca, Spain, known for its clear turquoise waters and family-friendly resort atmosphere.
-
C.
Emma Ferrer
Emma Ferrer is a Swiss-born artist, model, and humanitarian, best known as the granddaughter of iconic actress Audrey Hepburn.
-
D.
Blanca Parés
Blanca Parés is a Spanish actress best known for her role in Pedro Almodóvar’s acclaimed film "Julieta."
-
E.
Carmen Rabassa
Carmen Rabassa is known primarily as the wife of acclaimed American literary translator Gregory Rabassa.
- 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_69e0c47b4e8c81908c8076eaa4c8e4f2 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f11fc8c2108190b55ff1ba3badc9fb |
completed | April 28, 2026, 8:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0a5a03382c819096fc1806717b1c23 |
completed | May 18, 2026, 12:14 a.m. |
| NEDg | Description generation | batch_6a0a5a967d28819084a37207e17d4f68 |
completed | May 18, 2026, 12:17 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0a5b24f6708190bfe903dceb0e9210 |
completed | May 18, 2026, 12:19 a.m. |
Created at: April 16, 2026, 7:07 p.m.