Triple
T7259607
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Palm Springs International Airport |
E159614
|
entity |
| Predicate | isNamedAfter |
P63
|
FINISHED |
| Object | Palm Springs |
E190789
|
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: Palm Springs | Statement: [Palm Springs International Airport, isNamedAfter, Palm Springs]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Palm Springs Context triple: [Palm Springs International Airport, isNamedAfter, Palm Springs]
-
A.
Palm Springs
chosen
Palm Springs is a desert resort city in Southern California known for its mid-century modern architecture, hot springs, golf courses, and tourism.
-
B.
Palm Desert
Palm Desert is a resort city in Southern California known for its golf courses, upscale shopping, and desert landscapes.
-
C.
Rancho Mirage
Rancho Mirage is an affluent resort city in Southern California known for its golf courses, luxury hotels, and desert climate.
-
D.
Santa Ana
Santa Ana is a major city in Orange County, California, known as a dense urban and governmental center within the Greater Los Angeles metropolitan area.
-
E.
Santa Ana
Santa Ana is a barangay (village-level administrative division) within the highly urbanized city of Taguig in Metro Manila, Philippines.
- 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_69c68838f9948190875fd60b2351230c |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6eac5311c819094fc6880f3152813 |
completed | March 27, 2026, 8:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c810bb0d808190b6ade5592fce2c15 |
completed | March 28, 2026, 5:32 p.m. |
Created at: March 27, 2026, 2:57 p.m.