Triple
T7407162
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dora Sigerson Shorter |
E170902
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Dora |
E49540
|
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: Dora | Statement: [Dora Sigerson Shorter, givenName, Dora]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dora Context triple: [Dora Sigerson Shorter, givenName, Dora]
-
A.
Dora
chosen
Dora is the given name of Dora Sigerson Shorter, an Irish poet associated with the late 19th- and early 20th-century literary revival.
-
B.
Dora
Dora is a character in Jim Jarmusch’s film "Broken Flowers," known as one of Don Johnston’s former girlfriends whom he visits while searching for the mother of his alleged son.
-
C.
Dora Riparia
Dora Riparia is a river in northwestern Italy that flows through the city of Turin before joining the Po River.
-
D.
Dora the Explorer
Dora the Explorer is a popular animated children's television series featuring a young Latina girl who embarks on interactive adventures while teaching viewers basic problem-solving and Spanish vocabulary.
-
E.
Dora Luz
Dora Luz was a Mexican singer and actress best known for her musical performances in classic Disney films of the 1940s.
- 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_69c68a6010108190925e5284de022660 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f298f2388190afc944c9bc78749a |
completed | March 27, 2026, 9:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8111adbf48190a04df3cec1017b39 |
completed | March 28, 2026, 5:34 p.m. |
Created at: March 27, 2026, 3:10 p.m.