Triple
T13293951
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dalia Jumblatt |
E316629
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Dalia |
E392939
|
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: Dalia | Statement: [Dalia Jumblatt, givenName, Dalia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dalia Context triple: [Dalia Jumblatt, givenName, Dalia]
-
A.
Dalia
chosen
Dalia is a central love interest and salon owner in the comedy film "You Don’t Mess with the Zohan," portrayed as a strong, independent Palestinian woman who becomes romantically involved with the title character.
-
B.
Dalia
Dalia is a supporting character in Disney’s 2019 live-action adaptation of Aladdin, serving as Princess Jasmine’s handmaiden and close confidante.
-
C.
Lilia
Lilia is a feminine given name, often considered a variant of Lily and associated with the elegance and symbolism of the lily flower.
-
D.
Lela
Lela is a feminine given name used in various cultures, often as a variant of Leila or Layla.
-
E.
Milina
Milina is a seaside village in the Pelion region of central Greece, known for its tranquil beaches and views across the Pagasetic Gulf.
- 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_69d806b349908190a9a61dd9323bf153 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99078bcf0819083195fb556bcacb2 |
completed | April 11, 2026, 12:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f716d8ee2081908428339216c43b47 |
completed | May 3, 2026, 9:35 a.m. |
Created at: April 9, 2026, 9:28 p.m.