Triple
T8839709
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marie Claire |
E210354
|
entity |
| Predicate | circulationArea |
P82
|
FINISHED |
| Object | Asia |
E2127
|
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: Asia | Statement: [Marie Claire, circulationArea, Asia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Asia Context triple: [Marie Claire, circulationArea, Asia]
-
A.
Asia
Asia is a figure in Greek mythology, often considered an Oceanid nymph associated with the region that later bore her name.
-
B.
Asia
Asia is a figure in Greek mythology, often considered one of the Oceanids and associated with the region that later bore her name.
-
C.
Asia
Asia is a British rock supergroup formed in the early 1980s, known for its melodic progressive rock sound and hits like "Heat of the Moment."
-
D.
Asia
chosen
Asia is the world’s largest and most populous continent, encompassing diverse cultures, languages, and landscapes across the Eastern and Northern Hemispheres.
-
E.
Asianet
Asianet is a leading Malayalam-language television channel and entertainment network widely watched in the Indian state of Kerala and among the Malayali diaspora.
- 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_69ca8388549c819095fd94eadefbb007 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc60849f8c8190b7735defecf55a5d |
completed | April 1, 2026, 12:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfab596ef88190b538d15b0d71e176 |
completed | April 3, 2026, 11:58 a.m. |
Created at: March 30, 2026, 6:48 p.m.