Triple
T19328860
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saeb Salam |
E483432
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Salam |
—
|
NE NERFINISHED |
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: Salam | Statement: [Saeb Salam, familyName, Salam]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Salam Context triple: [Saeb Salam, familyName, Salam]
-
A.
Salam
chosen
Salam is a common Arabic-origin surname borne by numerous individuals across the world, including notable figures in science, politics, and the arts.
-
B.
Salam Affandina
Salam Affandina was the royal anthem of the Kingdom of Egypt, ceremonially associated with its monarchy and state occasions in the late 19th and early 20th centuries.
-
C.
Salutati
Salutati is an Italian surname most notably associated with Coluccio Salutati, a leading 14th-century humanist and Chancellor of Florence.
-
D.
Assalah
Assalah is a residential and beachside neighborhood in the Egyptian resort town of Dahab on the Sinai Peninsula.
-
E.
Lasalimu
Lasalimu is a coastal town and district in Southeast Sulawesi, Indonesia, known as a local administrative and transit area in the region.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8d13e3c81909d91d1d5ec37c095 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e6163ffddc81909e9cb13e780f1f18 |
completed | April 20, 2026, 12:04 p.m. |
Created at: April 10, 2026, 1:33 p.m.