Triple
T8750744
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Central Iowa |
E207951
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Altoona |
E418929
|
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: Altoona | Statement: [Central Iowa, hasCity, Altoona]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Altoona Context triple: [Central Iowa, hasCity, Altoona]
-
A.
Altoona
chosen
Altoona is a small suburban city in central Iowa known for its proximity to Des Moines and attractions like the Adventureland amusement park and Prairie Meadows casino and racetrack.
-
B.
Altoona
Altoona is a small town located in Etowah County in the state of Alabama, United States.
-
C.
Altoona, Pennsylvania
Altoona, Pennsylvania is a historic city in central Pennsylvania known for its deep ties to the railroad industry and its role as a major rail transportation hub.
-
D.
Mattersburg
Mattersburg is a small Austrian town that serves as an important local center in the eastern state of Burgenland.
-
E.
Meadville
Meadville is a small city in northwestern Pennsylvania known historically as an early center of industry and home to Allegheny College.
- 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_69ca835bb2bc819084bb5906cb6ef7f8 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5da774f4819099e5bfd12973d946 |
completed | March 31, 2026, 11:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf431db5a88190a579b43370a8e887 |
completed | April 3, 2026, 4:33 a.m. |
Created at: March 30, 2026, 6:39 p.m.