Triple
T8412332
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tammy Blanchard |
E198653
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Sybil |
E452197
|
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: Sybil | Statement: [Tammy Blanchard, notableWork, Sybil]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sybil Context triple: [Tammy Blanchard, notableWork, Sybil]
-
A.
Sybil
Sybil is a character from the fantasy film "The Magic Sword," known for her role in the story’s magical and adventurous narrative.
-
B.
Sybil
Sybil was an illegitimate daughter of King Henry I of England, known primarily through her royal lineage and connections within the Anglo-Norman nobility.
-
C.
Sybil
chosen
Sybil is an American R&B and pop singer best known for her late-1980s and early-1990s hits, including popular covers of classic soul songs.
-
D.
Sybylla
Sybylla is the spirited, independent-minded young heroine and narrator of Miles Franklin’s classic Australian novel "My Brilliant Career."
-
E.
Sybil, or The Two Nations
Sybil, or The Two Nations is an 1845 social and political novel by Benjamin Disraeli that explores the deep class divisions and harsh conditions of the English working poor during the Industrial Revolution.
- 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_69ca831201b481909e137936ef99ff11 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cb83e0341c819080506e696131671e |
completed | March 31, 2026, 8:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce0322d1448190aceaf7486c110ff7 |
completed | April 2, 2026, 5:48 a.m. |
Created at: March 30, 2026, 6:05 p.m.