Triple
T15907336
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amal Alamuddin |
E385753
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Amal |
E385754
|
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: Amal | Statement: [Amal Alamuddin, givenName, Amal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amal Context triple: [Amal Alamuddin, givenName, Amal]
-
A.
Amal
chosen
Amal is a feminine given name of Arabic origin meaning "hope" or "aspiration," used in various cultures around the world.
-
B.
Umamah
Umamah is a female given name of Arabic origin, historically borne by a granddaughter of the Prophet Muhammad.
-
C.
Ajami
Ajami is an adapted form of the Arabic script historically used to write various African languages, including Pulaar, for religious, literary, and administrative purposes.
-
D.
Al Raed
Al Raed is a professional football club based in Buraidah, Saudi Arabia, competing in the Saudi Pro League.
-
E.
Amaala
Amaala is a luxury tourism and wellness destination being developed on Saudi Arabia’s Red Sea coast as part of the country’s economic diversification and high-end tourism strategy.
- 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_69d86da686e4819097cbf3b1fc2d881d |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1565c11bc819091b1fd85901a832d |
completed | April 16, 2026, 9:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffb055307081908a13c98a0e16780c |
completed | May 9, 2026, 10:08 p.m. |
Created at: April 10, 2026, 4:52 a.m.