Triple
T7812455
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Count of Mark |
E180719
|
entity |
| Predicate | hasSeat |
P3522
|
FINISHED |
| Object | Hamm |
E149797
|
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: Hamm | Statement: [Count of Mark, hasSeat, Hamm]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hamm Context triple: [Count of Mark, hasSeat, Hamm]
-
A.
Hamm
chosen
Hamm is a city in North Rhine-Westphalia, Germany, known for its industrial heritage and strategic location in the Ruhr region.
-
B.
Hamm
Hamm is the wisecracking plastic piggy bank toy from the Toy Story film series, known for his sarcastic humor and loyalty to Andy’s other toys.
-
C.
Hanno the Great
Hanno the Great was a powerful Carthaginian statesman and military leader known for his influential role in Carthage’s politics during the Punic Wars era.
-
D.
Hammann
Hammann is a German-origin surname borne by various notable individuals in fields such as aviation, music, and academia.
-
E.
Hamura
Hamura is a city in western Tokyo, Japan, known for its residential neighborhoods and proximity to the Tama River.
- 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_69ca827f6f148190beca4e245b993506 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69caf78e198c81909d4fd227f6b71082 |
completed | March 30, 2026, 10:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb5a5048a88190874d7ff205151d8a |
completed | March 31, 2026, 5:23 a.m. |
Created at: March 30, 2026, 4:38 p.m.