Triple
T15811287
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Everwood |
E383358
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object | Amy Abbott |
E1219101
|
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: Amy Abbott | Statement: [Everwood, hasCharacter, Amy Abbott]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amy Abbott Context triple: [Everwood, hasCharacter, Amy Abbott]
-
A.
Amy Abbott
chosen
Amy Abbott is a central teenage character in the TV drama "Everwood," known for her emotional complexity, family struggles, and evolving relationship with Ephram Brown.
-
B.
Kate Abbot-Anderson
Kate Abbot-Anderson is a British woman best known as the former wife of comedian and actor Hugh Dennis.
-
C.
Sarah Abbott
Sarah Abbott is an actress known for her role in the horror film "The Silence."
-
D.
Rachel McCleary
Rachel McCleary is an American economist and scholar known for her work on the intersection of religion, culture, and economic development.
-
E.
Claire Dodd
Claire Dodd was an American film actress of the 1930s and 1940s, often cast as sophisticated or scheming society women in Hollywood productions.
- 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_69d86da2858c819090cc8481e7207b6e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e0b52bbb888190b226567e84ced7e9 |
completed | April 16, 2026, 10:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0067948b308190a434cdf1d45ebef4 |
completed | May 10, 2026, 11:10 a.m. |
Created at: April 10, 2026, 4:49 a.m.