Triple
T3287721
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dayton Callie |
E69022
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | ER |
E82125
|
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: ER | Statement: [Dayton Callie, notableWork, ER]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ER Context triple: [Dayton Callie, notableWork, ER]
-
A.
ER
chosen
ER is a critically acclaimed American medical drama television series that follows the personal and professional lives of staff in a busy Chicago emergency room.
-
B.
ER
ER is the vehicle registration code assigned to the German city of Erlangen in the state of Bavaria.
-
C.
ER
ER is the commonly used abbreviation for United Russia, the dominant ruling political party in the Russian Federation.
-
D.
RE
RE is the two-letter ISO 3166-1 alpha-2 country code assigned to the French overseas department and region of Réunion.
-
E.
RE
RE is the common abbreviation for the British Army’s Corps of Royal Engineers, responsible for military engineering and technical support.
- 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_69ad859d45748190b0742408c954b39f |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb058e00881908fdf0a23208860d4 |
completed | March 8, 2026, 5:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2f3cd5f508190ac5abc2dcdf0957d |
completed | March 12, 2026, 5:11 p.m. |
Created at: March 8, 2026, 3:10 p.m.