Triple
T8134073
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chambersburg, Pennsylvania |
E189925
|
entity |
| Predicate | foundedBy |
P104
|
FINISHED |
| Object | Benjamin Chambers |
E714106
|
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: Benjamin Chambers | Statement: [Chambersburg, Pennsylvania, foundedBy, Benjamin Chambers]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Benjamin Chambers Context triple: [Chambersburg, Pennsylvania, foundedBy, Benjamin Chambers]
-
A.
Benjamin Chambers
chosen
Benjamin Chambers was an early American settler and mill owner who founded the town that became Chambersburg, Pennsylvania.
-
B.
Samuel Pearson
Samuel Pearson was a British entrepreneur and publisher best known for establishing the company that evolved into the global education and publishing corporation Pearson plc.
-
C.
Samuel Ward
Samuel Ward was a 19th-century American banker and art patron known for commissioning significant works such as Thomas Cole’s "The Voyage of Life" series.
-
D.
Nathaniel Burke
Nathaniel Burke is the primary villain in the 1997 superhero film "Steel," serving as the main adversary to the armored hero John Henry Irons.
-
E.
Nathaniel Chambers
Nathaniel Chambers is a computer scientist known for his work in natural language processing, particularly in narrative event understanding and script learning.
- 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_69ca82bcb4848190a9a9d036ad768642 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb43bbae608190bdc1afe6f0ab83ae |
completed | March 31, 2026, 3:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ccbec8491c81908362d44c42fc8568 |
completed | April 1, 2026, 6:44 a.m. |
Created at: March 30, 2026, 5:35 p.m.