Triple
T13448909
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Geoff Mercer |
E320555
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Kate Mercer |
E1040259
|
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: Kate Mercer | Statement: [Geoff Mercer, spouse, Kate Mercer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kate Mercer Context triple: [Geoff Mercer, spouse, Kate Mercer]
-
A.
Kate Mercer
chosen
Kate Mercer is the introspective, long-married woman at the center of the British drama film "45 Years," whose life is upended by revelations from her husband's past.
-
B.
Christina Mercer
Christina Mercer is a contemporary author known for writing young adult and fantasy fiction.
-
C.
Meredith Baxter
Meredith Baxter is an American actress best known for her role as Elyse Keaton on the 1980s television sitcom "Family Ties."
-
D.
Sarah O’Meara
Sarah O’Meara is known as the spouse of Australian film director Paul Cox.
-
E.
Jane Bingum
Jane Bingum is the intelligent, plus-sized lawyer protagonist of the television series "Drop Dead Diva," known for combining sharp legal skills with a compassionate, quirky personality.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d80761e6cc8190a90c844589998ecc |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaef758b08190b9aa5ec7082cd417 |
completed | April 12, 2026, 2:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7462340548190b156a8213f5e2556 |
completed | May 3, 2026, 12:57 p.m. |
Created at: April 9, 2026, 9:41 p.m.