Triple
T7559672
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Angel |
E178761
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Angeles |
E715
|
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: Angeles | Statement: [Angel, hasVariant, Angeles]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Angeles Context triple: [Angel, hasVariant, Angeles]
-
A.
Los Ángeles
Los Ángeles is a mid-sized Chilean city known as an important commercial and agricultural center in the south-central part of the country.
-
B.
San Angeles
San Angeles is a fictional futuristic megacity formed from the merger of Los Angeles and San Diego in the science fiction film "Demolition Man."
-
C.
San Cristóbal de los Ángeles
San Cristóbal de los Ángeles is a neighborhood in Madrid, Spain, known in part for its local football club that helped develop future Real Madrid legend Raúl González during his youth.
-
D.
Los Angeles
chosen
Los Angeles is a major U.S. metropolis known for its entertainment industry, cultural diversity, and sprawling urban landscape.
-
E.
Angeles City
Angeles City is a highly urbanized city in the Philippines’ Pampanga province, known as a commercial and cultural hub in Central Luzon.
- 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_69c69f2da22c8190a50942ac20af70e8 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f8dd96488190b4cca25ae8f7f95c |
completed | March 27, 2026, 9:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c856cc869081909555ae03dec52288 |
completed | March 28, 2026, 10:31 p.m. |
Created at: March 27, 2026, 3:50 p.m.