Triple
T14418145
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris Hilton |
E357509
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Hilton |
E1003741
|
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: Hilton | Statement: [Paris Hilton, familyName, Hilton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hilton Context triple: [Paris Hilton, familyName, Hilton]
-
A.
Hilton
Hilton is a village and civil parish in South Derbyshire, England, known for its rapid modern expansion and residential developments.
-
B.
Hilton
chosen
Hilton is a global hospitality company that operates a worldwide portfolio of hotels and resorts across multiple brands.
-
C.
Hilton
Hilton is an inner-western suburb of Adelaide in South Australia, known for its proximity to the city centre and mixed residential–commercial character.
-
D.
Hyatt
Hyatt is a surname most notably associated with Alpheus Hyatt, an American zoologist and paleontologist known for his work on evolutionary theory and cephalopods.
-
E.
Hyatt
Hyatt is a global hospitality company known for its upscale and luxury hotels, resorts, and branded properties around the world.
- 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_69d82793421c8190861eb0e673b085de |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de90cec8e4819087c72d82f9caacda |
completed | April 14, 2026, 7:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd5bc961c48190b67dceb2f07977fd |
completed | May 8, 2026, 3:43 a.m. |
Created at: April 10, 2026, 1:17 a.m.