Triple
T17352796
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Naboo |
E421856
|
entity |
| Predicate | notableCity |
P2813
|
FINISHED |
| Object | Keren |
—
|
NE ONNED1 |
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: Keren | Statement: [Naboo, notableCity, Keren]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Keren Context triple: [Naboo, notableCity, Keren]
-
A.
Keren
chosen
Keren is a major town in Eritrea known as an important commercial and agricultural center in the Anseba region.
-
B.
Keren Rosenberg
Keren Rosenberg is a film producer known for her work on the adaptation of Amos Oz’s autobiographical novel "A Tale of Love and Darkness."
-
C.
Sharona Katan
Sharona Katan is an Israeli-born visual artist and the wife of Radiohead guitarist and composer Jonny Greenwood.
-
D.
Keira Hagai
Keira Hagai is a skilled mechanic and hovercraft engineer who serves as Jak’s close ally and love interest in the Jak and Daxter video game series.
-
E.
Daliah Lavi
Daliah Lavi was an Israeli actress, singer, and model best known internationally for her roles in 1960s European and Hollywood films, particularly in stylish spy spoofs and adventure movies.
- 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_69d889d520008190a26917a95bf1c2ea |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a2dae648190b7f3487919a446af |
completed | April 19, 2026, 2:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01955a50dc819090c1a0ec111d9fc0 |
in_progress | May 11, 2026, 8:37 a.m. |
Created at: April 10, 2026, 5:44 a.m.