Triple
T14116640
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Always |
E339791
|
entity |
| Predicate | notableProduct |
P1448
|
FINISHED |
| Object | Always Maxi pads |
E726040
|
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: Always Maxi pads | Statement: [Always, notableProduct, Always Maxi pads]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Always Maxi pads Context triple: [Always, notableProduct, Always Maxi pads]
-
A.
Sheex
Sheex is a performance bedding company known for its athletic-inspired, moisture-wicking sheets and sleepwear.
-
B.
Kotex
chosen
Kotex is a well-known feminine hygiene brand offering products such as sanitary pads, tampons, and liners.
-
C.
Wacoal
Wacoal is a Japanese company best known as a leading manufacturer and retailer of women's lingerie and intimate apparel.
-
D.
MAXI
MAXI is an X-ray astronomy mission that continuously monitors the sky for high-energy phenomena such as black hole outbursts, neutron star activity, and other transient cosmic events.
-
E.
Diaper Dandy
Diaper Dandy is a famous Dick Vitale catchphrase referring to an outstanding freshman college basketball player.
- 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_69d81c6a95b481909e39111e0c1f31ee |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de6010a03c81909f5f160f8d1fa8fa |
completed | April 14, 2026, 3:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcd0baa328819099511dfa7b9666d3 |
completed | May 7, 2026, 5:49 p.m. |
Created at: April 9, 2026, 10:22 p.m.