Triple
T28321449
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nina Wadia |
E717288
|
entity |
| Predicate | hasRole |
P161
|
FINISHED |
| Object |
Zainab Masood
Zainab Masood is a fictional character from the British soap opera "EastEnders," known as the strong-willed and sharp-tongued matriarch of the Masood family.
|
E1813213
|
NE FINISHED |
How this triple was built (2 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: Zainab Masood | Statement: [Nina Wadia, hasRole, Zainab Masood]
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: Zainab Masood Triple: [Nina Wadia, hasRole, Zainab Masood]
Generated description
Zainab Masood is a fictional character from the British soap opera "EastEnders," known as the strong-willed and sharp-tongued matriarch of the Masood family.
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_69eff6e6c3b08190ad78de6ba7f04548 |
completed | April 27, 2026, 11:53 p.m. |
| NER | Named-entity recognition | batch_69f6492aa8e0819094ac7e735e6955a7 |
completed | May 2, 2026, 6:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a1627af3e288190b45b278072683d8b |
completed | May 26, 2026, 11:07 p.m. |
| NEDg | Description generation | batch_6a1628538278819081117ceea1291326 |
completed | May 26, 2026, 11:10 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a1628d9a6fc819089b0925c9912b65f |
completed | May 26, 2026, 11:12 p.m. |
Created at: April 28, 2026, 12:24 a.m.