Triple
T7716033
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lotusmiles |
E174884
|
entity |
| Predicate | hasTier |
P2393
|
FINISHED |
| Object | Silver |
E644854
|
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: Silver | Statement: [Lotusmiles, hasTier, Silver]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Silver Context triple: [Lotusmiles, hasTier, Silver]
-
A.
Silver
Silver is a lustrous, highly conductive precious metal widely used in jewelry, industry, and currency throughout history.
-
B.
Silver
chosen
Silver is a mid-level frequent flyer status tier that offers travelers enhanced benefits and privileges over the basic membership level.
-
C.
Gold
Gold is a 2016 American crime adventure film in which Matthew McConaughey stars as a prospector chasing a potentially fraudulent gold discovery in the Indonesian jungle.
-
D.
Gold
Gold was the codename for one of the five Allied landing beaches used by British forces during the D-Day invasion of Normandy in World War II.
-
E.
Gold
Gold is a chemical element and precious metal highly valued for its rarity, luster, and use in jewelry, currency, and electronics.
- 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_69c6995c463c8190a14458036249d419 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c702cd0ddc8190aa23d998f55d0bd6 |
completed | March 27, 2026, 10:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8b50cf3208190af9bb2d4126d381b |
completed | March 29, 2026, 5:13 a.m. |
Created at: March 27, 2026, 4:04 p.m.