Triple
T19634083
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tom B. Brown |
E471343
|
entity |
| Predicate | coAuthorWith |
P398
|
FINISHED |
| Object | Melanie Subbiah |
—
|
NE NERFINISHED |
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: Melanie Subbiah | Statement: [Tom B. Brown, coAuthorWith, Melanie Subbiah]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Melanie Subbiah Context triple: [Tom B. Brown, coAuthorWith, Melanie Subbiah]
-
A.
Melanie Subbiah
chosen
Melanie Subbiah is an AI researcher known for co-authoring influential work in large language models and natural language processing.
-
B.
Mathangi Arulpragasam
Mathangi Arulpragasam, better known by her stage name M.I.A., is a British-Sri Lankan rapper, singer, producer, and visual artist renowned for her politically charged, genre-blending music and global cultural influence.
-
C.
Maya Banerjee
Maya Banerjee is known as the wife of Indian actor Victor Banerjee.
-
D.
Geraldine Viswanathan
Geraldine Viswanathan is an Australian actress known for her breakout comedic roles in films like "Blockers" and for her work in television, including the anthology series "Miracle Workers."
-
E.
Sheila Shah
Sheila Shah is an actress known for her role in the action film "Expend4bles" and appearances in other film and television projects.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e511f28481909f4bc3ea9191e54a |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e64104ff2881908fec49b7fba5a2e6 |
completed | April 20, 2026, 3:06 p.m. |
Created at: April 10, 2026, 1:44 p.m.