Triple
T2905498
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jean Speegle Howard |
E62751
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Matilda |
E310759
|
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: Matilda | Statement: [Jean Speegle Howard, notableWork, Matilda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matilda Context triple: [Jean Speegle Howard, notableWork, Matilda]
-
A.
Matilda
Matilda is a virtuous and tragic noblewoman in Horace Walpole’s pioneering Gothic novel "The Castle of Otranto."
-
B.
Matilda
Matilda was the regnal name of Edith of Scotland, who became Queen consort of England as the wife of King Henry I.
-
C.
Matilda
Matilda is the middle name of Louisa Matilda Jacobs, an African American writer and activist and the daughter of abolitionist author Harriet Jacobs.
-
D.
Matilda
chosen
Matilda is a 1996 family fantasy-comedy film, based on Roald Dahl’s novel, about a gifted young girl with telekinetic powers who stands up to her cruel parents and tyrannical school principal.
-
E.
Malory Towers
Malory Towers is a classic British children's book series set in a girls' boarding school, written by Enid Blyton and known for its stories of friendship, school life, and personal growth.
- 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_69ab4c3e070c8190b78d3d2c005876dd |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abe0cee7988190875665145c3cd605 |
completed | March 7, 2026, 8:24 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b08658407c8190ad7798590dd17ef9 |
completed | March 10, 2026, 9 p.m. |
Created at: March 6, 2026, 10:11 p.m.