Triple
T14667247
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | If You Had My Love |
E344409
|
entity |
| Predicate | certificationUK |
P4914
|
FINISHED |
| Object | Silver |
E16227
|
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: [If You Had My Love, certificationUK, Silver]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Silver Context triple: [If You Had My Love, certificationUK, Silver]
-
A.
Silver
Silver is the iconic white stallion famously ridden by the masked Western hero the Lone Ranger.
-
B.
Silver
chosen
Silver is a lustrous, highly conductive precious metal widely used in jewelry, industry, and currency throughout history.
-
C.
Silver
Silver is a mid-level frequent flyer status tier that offers travelers enhanced benefits and privileges over the basic membership level.
-
D.
silver zarih
The silver zarih is an ornate, silver-encased lattice structure that surrounds and marks the sacred burial site within the Al-Abbas Shrine in Karbala.
-
E.
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.
- 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_69d822e283fc8190a0e4c235cf880052 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb54dda1c8190bf16d17e26a2bba6 |
completed | April 14, 2026, 9:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdd5e63e6c8190ba67776719f2ff0c |
completed | May 8, 2026, 12:24 p.m. |
Created at: April 10, 2026, 1:27 a.m.