Triple
T5846958
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shah Hussain |
E129734
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Hussain |
E303381
|
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: Hussain | Statement: [Shah Hussain, givenName, Hussain]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hussain Context triple: [Shah Hussain, givenName, Hussain]
-
A.
Hussain
chosen
Hussain is a common given name and surname of Arabic origin, widely used in Muslim communities around the world.
-
B.
Habib
Habib is a masculine given name of Arabic origin commonly used in North Africa and the Middle East.
-
C.
Hussaini
Hussaini is a small, picturesque village in Pakistan’s Hunza Valley, best known for its dramatic mountain scenery and the famous, vertigo-inducing Hussaini suspension bridge.
-
D.
Hassan
Hassan is a key antagonist in Lord Byron’s narrative poem "The Giaour," depicted as a powerful Ottoman leader whose actions drive the poem’s central conflict.
-
E.
Hassan
Hassan is a loyal and selfless Hazara boy whose friendship with Amir and the injustices he endures form the emotional core of the film "The Kite Runner."
- 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_69c0084bd31c8190a796bb6284845e83 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0351157508190a78d2a7141e0cee8 |
completed | March 22, 2026, 6:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c16e8061008190bab55d4f7cf38d04 |
completed | March 23, 2026, 4:46 p.m. |
Created at: March 22, 2026, 3:55 p.m.