Triple
T3435506
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Romana |
E72441
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object |
Lalla Ward
Lalla Ward is an English actress and author best known for playing the Time Lady Romana in the long-running British science fiction series Doctor Who.
|
E357154
|
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: Lalla Ward | Statement: [Romana, portrayedBy, Lalla Ward]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lalla Ward Context triple: [Romana, portrayedBy, Lalla Ward]
-
A.
Catherine Durkan
Catherine Durkan is a notable individual associated with the Durkan family name, recognized as a bearer of this surname.
-
B.
Jane Wenham
Jane Wenham was a British actress known for her work in mid-20th-century film, television, and theatre.
-
C.
Lara Pulver
Lara Pulver is a British actress known for her roles in television series such as "Sherlock" and "Spooks," as well as various film and stage productions.
-
D.
Victoria Tennant
Victoria Tennant is a British actress known for her work in film and television, including roles in "L.A. Story" and the miniseries "The Winds of War."
-
E.
Tessa Menzies
Tessa Menzies is a child of California politician and governor Gavin Newsom.
- 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: Lalla Ward Triple: [Romana, portrayedBy, Lalla Ward]
Generated description
Lalla Ward is an English actress and author best known for playing the Time Lady Romana in the long-running British science fiction series Doctor Who.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lalla Ward Target entity description: Lalla Ward is an English actress and author best known for playing the Time Lady Romana in the long-running British science fiction series Doctor Who.
-
A.
Catherine Durkan
Catherine Durkan is a notable individual associated with the Durkan family name, recognized as a bearer of this surname.
-
B.
Jane Wenham
Jane Wenham was a British actress known for her work in mid-20th-century film, television, and theatre.
-
C.
Lara Pulver
Lara Pulver is a British actress known for her roles in television series such as "Sherlock" and "Spooks," as well as various film and stage productions.
-
D.
Victoria Tennant
Victoria Tennant is a British actress known for her work in film and television, including roles in "L.A. Story" and the miniseries "The Winds of War."
-
E.
Tessa Menzies
Tessa Menzies is a child of California politician and governor Gavin Newsom.
- 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_69ad85af50288190a854b76653deee6f |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb9f2e4b4819085336fb539daf3c7 |
completed | March 8, 2026, 6:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b35481a03c81908fd69da55d81aca3 |
completed | March 13, 2026, 12:04 a.m. |
| NEDg | Description generation | batch_69b355a7dc308190ba8ab0db251592a2 |
completed | March 13, 2026, 12:09 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b3567042ac8190aacf4e30da1aa816 |
completed | March 13, 2026, 12:12 a.m. |
Created at: March 8, 2026, 3:16 p.m.