Triple
T19842759
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Farhat Hached |
E476777
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Farhat |
—
|
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: Farhat | Statement: [Farhat Hached, givenName, Farhat]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Farhat Context triple: [Farhat Hached, givenName, Farhat]
-
A.
Farhat
chosen
Farhat is a surname of Arabic origin borne by various individuals, including Tunisian figures such as Chadlia Saïda Farhat.
-
B.
Jaffar
Jaffar is the sinister vizier and main antagonist portrayed by Conrad Veidt in the 1940 fantasy film "The Thief of Bagdad."
-
C.
Fattah
Fattah is the surname of Chaka Fattah, a former U.S. Congressman from Pennsylvania known for his long tenure in the House of Representatives and subsequent corruption conviction.
-
D.
Hafik
Hafik is a small town and district in central Turkey known for its rural character and location within Sivas Province in Central Anatolia.
-
E.
Al-Hareeq
Al-Hareeq is a town in central Saudi Arabia known for its agricultural activity, particularly date palm cultivation, within the Riyadh 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_69d8e51d39d081909bcfafeaaf3d2fcc |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65806ea888190850421154238d91c |
completed | April 20, 2026, 4:44 p.m. |
Created at: April 10, 2026, 1:51 p.m.