Triple
T22979752
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CSS Multi-column Layout Module Level 1 |
E571425
|
entity |
| Predicate | editor |
P1954
|
FINISHED |
| Object | Elika J. Etemad |
—
|
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: Elika J. Etemad | Statement: [CSS Multi-column Layout Module Level 1, editor, Elika J. Etemad]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elika J. Etemad Context triple: [CSS Multi-column Layout Module Level 1, editor, Elika J. Etemad]
-
A.
Elika J. Etemad
chosen
Elika J. Etemad, also known as "fantasai," is a prominent web standards expert and specification editor heavily involved in the development of CSS for the World Wide Web Consortium (W3C).
-
B.
Katayoun Amjadi
Katayoun Amjadi is known as the spouse of Iranian actor Behrouz Vossoughi.
-
C.
Sia Alipour
Sia Alipour is an actor known for his role in the Iranian television series "Tehran."
-
D.
Taraneh Alidoosti
Taraneh Alidoosti is an acclaimed Iranian actress known for her leading roles in contemporary Iranian cinema and frequent collaborations with director Asghar Farhadi.
-
E.
Arta Tabaee
Arta Tabaee is a senior executive and key investment professional at the private equity firm Clearlake Capital.
- 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_69e245b3c50481908bb3741ec9f40862 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18294c4c8819083ef85d9cb736613 |
completed | April 29, 2026, 4:01 a.m. |
Created at: April 17, 2026, 3:49 p.m.