Triple
T1312430
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sloan Park |
E28021
|
entity |
| Predicate | city |
P40
|
FINISHED |
| Object | Mesa |
E19620
|
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: Mesa | Statement: [Sloan Park, city, Mesa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mesa Context triple: [Sloan Park, city, Mesa]
-
A.
Mesa
Mesa is a pioneering systems programming language developed at Xerox PARC in the 1970s, notable for its strong typing, modularity, and influence on later languages and operating system design.
-
B.
Mesa, Arizona
chosen
Mesa, Arizona is a large city in the Phoenix metropolitan area known for its desert climate, suburban communities, and role as a major spring training hub for Major League Baseball.
-
C.
Sedona
Sedona is a scenic Arizona city famed for its striking red rock formations, vibrant arts community, and reputation as a spiritual and outdoor recreation destination.
-
D.
San Luis
San Luis is a residential and commercial district located in the eastern part of Lima, Peru.
-
E.
San Luis
San Luis is a municipality and town in western Cuba known for its agricultural activities within Pinar del Río Province.
- 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_69a498532c3481909223b74af2e578df |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c1572b1c8190ab978198c2d655c8 |
completed | March 1, 2026, 10:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad08a5b5fc81909b75f07b620447cd |
completed | March 8, 2026, 5:27 a.m. |
Created at: March 1, 2026, 7:55 p.m.