Triple
T3900749
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | OCLC |
E90481
|
entity |
| Predicate | headquartersLocation |
P62
|
FINISHED |
| Object | Dublin, Ohio |
E403147
|
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: Dublin, Ohio | Statement: [OCLC, headquartersLocation, Dublin, Ohio]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dublin, Ohio Context triple: [OCLC, headquartersLocation, Dublin, Ohio]
-
A.
Dublin, Ohio
chosen
Dublin, Ohio is a suburban city northwest of Columbus known for its affluent neighborhoods, strong school system, and annual Dublin Irish Festival.
-
B.
Havana, Ohio
Havana, Ohio is a small unincorporated community located in Huron County in north-central Ohio.
-
C.
Geneva, Ohio
Geneva, Ohio is a small city in northeastern Ohio best known as the birthplace of automobile pioneer Ransom E. Olds.
-
D.
Montgomery, Ohio
Montgomery, Ohio is a suburban city in Hamilton County near Cincinnati, known for its historic charm, affluent residential character, and well-regarded schools.
-
E.
Harrisburg, Ohio
Harrisburg, Ohio is a small village in central Ohio that functions as part of the Columbus metropolitan area.
- 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_69aed95d315881908cbf1bf4a7215fbf |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeecf2f230819099abc109a0b7d916 |
completed | March 9, 2026, 3:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be5c69987881908dc2b6286fec73c2 |
completed | March 21, 2026, 8:52 a.m. |
Created at: March 9, 2026, 3:21 p.m.