Triple
T16297755
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rafael Yglesias |
E395693
|
entity |
| Predicate | authorOf |
P4244
|
FINISHED |
| Object | Hot Properties |
E1205956
|
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: Hot Properties | Statement: [Rafael Yglesias, authorOf, Hot Properties]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hot Properties Context triple: [Rafael Yglesias, authorOf, Hot Properties]
-
A.
Hot Properties
chosen
Hot Properties is a novel by American writer Rafael Yglesias, known for its sharp, contemporary exploration of relationships and urban life.
-
B.
HOT
HOT is the three-letter National Rail station code assigned to Henley-on-Thames railway station in Oxfordshire, England.
-
C.
HOT
HOT is the stock ticker symbol for Hochtief, a major German construction and infrastructure company.
-
D.
HOT
HOT is the vehicle registration code used on license plates for vehicles registered in the Zwickau district of Germany.
-
E.
Hot Stuff
"Hot Stuff" is a 1979 disco hit by Donna Summer that blends dance rhythms with rock influences and became one of her signature songs.
- 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_69d87f23bb088190a16fbb91a1957ea5 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e25e2f486c8190b73c15f59335cde2 |
completed | April 17, 2026, 4:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0025ff9020819088f2146bdbfb2e2a |
completed | May 10, 2026, 6:30 a.m. |
Created at: April 10, 2026, 5:06 a.m.