Triple
T5223044
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | OLIN |
E117917
|
entity |
| Predicate | formerName |
P65
|
FINISHED |
| Object | Hanna/Olin |
E117917
|
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: Hanna/Olin | Statement: [OLIN, formerName, Hanna/Olin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hanna/Olin Context triple: [OLIN, formerName, Hanna/Olin]
-
A.
OLIN
chosen
OLIN is a prominent landscape architecture and urban design firm known for shaping major public spaces and environmentally responsive projects worldwide.
-
B.
Kedzie–Homan
Kedzie–Homan is a Chicago Transit Authority rapid transit station on the West Side serving the Blue Line’s Congress Branch.
-
C.
Hanley
Hanley is one of the main towns that make up the city of Stoke-on-Trent in Staffordshire, England, known historically for its role in the pottery industry.
-
D.
Olson
Olson is a surname most prominently associated in entertainment with American actress and comedian Kaitlin Olson.
-
E.
Hannan
Hannan is a coastal city in southern Osaka Prefecture, Japan, known for its fishing industry and proximity to Osaka Bay.
- 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_69bd4465e03081909bfcfd7113062590 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7abba82881908c030ba55146b8ea |
completed | March 20, 2026, 4:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69beeff852bc81908467a343c5ded404 |
completed | March 21, 2026, 7:22 p.m. |
Created at: March 20, 2026, 1:48 p.m.