Triple
T7441855
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oscar De La Hoya |
E171773
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Oscar |
E359307
|
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: Oscar | Statement: [Oscar De La Hoya, givenName, Oscar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oscar Context triple: [Oscar De La Hoya, givenName, Oscar]
-
A.
Oscar
The Oscar is a prestigious film industry award presented annually by the Academy of Motion Picture Arts and Sciences to honor outstanding cinematic achievements.
-
B.
Oscar
Oscar is the Allied reporting name for the Nakajima Ki-43, a Japanese World War II fighter aircraft used extensively by the Imperial Japanese Army Air Service.
-
C.
Oscar
chosen
Oscar is a masculine given name of Old English and Norse origin, commonly used in many European and English-speaking countries.
-
D.
OSCAR
OSCAR is the proprietary messaging protocol developed by AOL to power its real-time chat and presence services across products like AIM and ICQ.
-
E.
Orson
Orson is a masculine given name most famously associated with the American filmmaker and actor Orson Welles.
- 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_69c68a64228c8190affaec2a8127ce7b |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f36b9a3c81908abcc2a64d3e6061 |
completed | March 27, 2026, 9:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c827976b9081909ac722b397ae0d97 |
completed | March 28, 2026, 7:10 p.m. |
Created at: March 27, 2026, 3:13 p.m.