Triple
T15630937
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aahoo Jahansouzshahi |
E375808
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Aahoo |
E375809
|
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: Aahoo | Statement: [Aahoo Jahansouzshahi, givenName, Aahoo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aahoo Context triple: [Aahoo Jahansouzshahi, givenName, Aahoo]
-
A.
Aahoo
chosen
Aahoo is the Persian birth name of American actress and former NFL cheerleader Sarah Shahi.
-
B.
Ahay
Ahay is a component or segment within the larger work "Fever Dream," likely representing a distinct chapter, track, or thematic section of that creative piece.
-
C.
Ahan
Ahan is a lesser-known Niger-Congo language spoken in parts of Nigeria, closely related to and geographically adjacent to Ukaan.
-
D.
Agoo
Agoo is a coastal municipality in the province of La Union, Philippines, known for its fishing communities, beaches, and historical churches along the Lingayen Gulf.
-
E.
Hau
Hau is the surname of Danish physicist Lene Vestergaard Hau, known for her pioneering work in slowing and stopping light.
- 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_69d85cd035a48190b73d5579ab73969a |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04eb536348190b93ed3c178d1ffb8 |
completed | April 16, 2026, 2:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff5f44f0b881909ce36823e4314799 |
completed | May 9, 2026, 4:22 p.m. |
Created at: April 10, 2026, 4:14 a.m.